Overview

Dataset statistics

Number of variables62
Number of observations90
Missing cells2203
Missing cells (%)39.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.7 KiB
Average record size in memory497.4 B

Variable types

Numeric13
Categorical40
Unsupported9

Alerts

airdate has constant value "2020-12-23" Constant
_embedded.show.dvdCountry.name has constant value "Ukraine" Constant
_embedded.show.dvdCountry.code has constant value "UA" Constant
_embedded.show.dvdCountry.timezone has constant value "Europe/Zaporozhye" Constant
url has a high cardinality: 90 distinct values High cardinality
name has a high cardinality: 76 distinct values High cardinality
_links.self.href has a high cardinality: 90 distinct values High cardinality
_embedded.show.url has a high cardinality: 65 distinct values High cardinality
_embedded.show.name has a high cardinality: 65 distinct values High cardinality
_embedded.show.premiered has a high cardinality: 54 distinct values High cardinality
_embedded.show.officialSite has a high cardinality: 55 distinct values High cardinality
_embedded.show.image.medium has a high cardinality: 63 distinct values High cardinality
_embedded.show.image.original has a high cardinality: 63 distinct values High cardinality
_embedded.show.summary has a high cardinality: 56 distinct values High cardinality
_embedded.show._links.self.href has a high cardinality: 65 distinct values High cardinality
_embedded.show._links.previousepisode.href has a high cardinality: 65 distinct values High cardinality
id is highly correlated with _embedded.show.id and 3 other fieldsHigh correlation
season is highly correlated with rating.average and 4 other fieldsHigh correlation
number is highly correlated with rating.average and 2 other fieldsHigh correlation
runtime is highly correlated with _embedded.show.runtime and 2 other fieldsHigh correlation
rating.average is highly correlated with season and 4 other fieldsHigh correlation
_embedded.show.id is highly correlated with id and 6 other fieldsHigh correlation
_embedded.show.runtime is highly correlated with runtime and 2 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with runtime and 2 other fieldsHigh correlation
_embedded.show.rating.average is highly correlated with id and 6 other fieldsHigh correlation
_embedded.show.weight is highly correlated with _embedded.show.rating.average and 2 other fieldsHigh correlation
_embedded.show.webChannel.id is highly correlated with _embedded.show.rating.average and 2 other fieldsHigh correlation
_embedded.show.externals.tvrage is highly correlated with id and 12 other fieldsHigh correlation
_embedded.show.externals.thetvdb is highly correlated with season and 4 other fieldsHigh correlation
_embedded.show.updated is highly correlated with rating.average and 2 other fieldsHigh correlation
_embedded.show.network.id is highly correlated with id and 8 other fieldsHigh correlation
id is highly correlated with _embedded.show.rating.average and 1 other fieldsHigh correlation
season is highly correlated with number and 3 other fieldsHigh correlation
number is highly correlated with season and 5 other fieldsHigh correlation
runtime is highly correlated with season and 5 other fieldsHigh correlation
rating.average is highly correlated with number and 1 other fieldsHigh correlation
_embedded.show.id is highly correlated with runtime and 4 other fieldsHigh correlation
_embedded.show.runtime is highly correlated with season and 4 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded.show.rating.average is highly correlated with id and 5 other fieldsHigh correlation
_embedded.show.weight is highly correlated with _embedded.show.rating.average and 2 other fieldsHigh correlation
_embedded.show.webChannel.id is highly correlated with _embedded.show.externals.tvrage and 1 other fieldsHigh correlation
_embedded.show.externals.tvrage is highly correlated with id and 10 other fieldsHigh correlation
_embedded.show.externals.thetvdb is highly correlated with runtime and 3 other fieldsHigh correlation
_embedded.show.updated is highly correlated with _embedded.show.rating.average and 2 other fieldsHigh correlation
_embedded.show.network.id is highly correlated with number and 6 other fieldsHigh correlation
id is highly correlated with _embedded.show.id and 1 other fieldsHigh correlation
season is highly correlated with rating.average and 2 other fieldsHigh correlation
number is highly correlated with rating.average and 2 other fieldsHigh correlation
runtime is highly correlated with _embedded.show.runtime and 1 other fieldsHigh correlation
rating.average is highly correlated with season and 2 other fieldsHigh correlation
_embedded.show.id is highly correlated with id and 4 other fieldsHigh correlation
_embedded.show.runtime is highly correlated with runtime and 2 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded.show.rating.average is highly correlated with rating.average and 4 other fieldsHigh correlation
_embedded.show.weight is highly correlated with _embedded.show.rating.average and 2 other fieldsHigh correlation
_embedded.show.webChannel.id is highly correlated with _embedded.show.rating.average and 2 other fieldsHigh correlation
_embedded.show.externals.tvrage is highly correlated with id and 10 other fieldsHigh correlation
_embedded.show.externals.thetvdb is highly correlated with season and 2 other fieldsHigh correlation
_embedded.show.updated is highly correlated with _embedded.show.externals.tvrage and 1 other fieldsHigh correlation
_embedded.show.network.id is highly correlated with number and 5 other fieldsHigh correlation
id is highly correlated with url and 27 other fieldsHigh correlation
url is highly correlated with id and 47 other fieldsHigh correlation
name is highly correlated with id and 45 other fieldsHigh correlation
season is highly correlated with url and 23 other fieldsHigh correlation
number is highly correlated with url and 30 other fieldsHigh correlation
type is highly correlated with url and 24 other fieldsHigh correlation
airtime is highly correlated with id and 41 other fieldsHigh correlation
airstamp is highly correlated with url and 43 other fieldsHigh correlation
runtime is highly correlated with url and 39 other fieldsHigh correlation
summary is highly correlated with id and 38 other fieldsHigh correlation
rating.average is highly correlated with url and 25 other fieldsHigh correlation
image.medium is highly correlated with id and 39 other fieldsHigh correlation
image.original is highly correlated with id and 39 other fieldsHigh correlation
_links.self.href is highly correlated with id and 47 other fieldsHigh correlation
_embedded.show.id is highly correlated with url and 41 other fieldsHigh correlation
_embedded.show.url is highly correlated with id and 47 other fieldsHigh correlation
_embedded.show.name is highly correlated with id and 47 other fieldsHigh correlation
_embedded.show.type is highly correlated with url and 39 other fieldsHigh correlation
_embedded.show.language is highly correlated with url and 41 other fieldsHigh correlation
_embedded.show.status is highly correlated with url and 35 other fieldsHigh correlation
_embedded.show.runtime is highly correlated with url and 40 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with url and 42 other fieldsHigh correlation
_embedded.show.premiered is highly correlated with id and 47 other fieldsHigh correlation
_embedded.show.ended is highly correlated with id and 35 other fieldsHigh correlation
_embedded.show.officialSite is highly correlated with id and 47 other fieldsHigh correlation
_embedded.show.schedule.time is highly correlated with url and 34 other fieldsHigh correlation
_embedded.show.rating.average is highly correlated with url and 19 other fieldsHigh correlation
_embedded.show.weight is highly correlated with id and 41 other fieldsHigh correlation
_embedded.show.webChannel.id is highly correlated with url and 36 other fieldsHigh correlation
_embedded.show.webChannel.name is highly correlated with id and 43 other fieldsHigh correlation
_embedded.show.webChannel.officialSite is highly correlated with id and 35 other fieldsHigh correlation
_embedded.show.externals.tvrage is highly correlated with url and 22 other fieldsHigh correlation
_embedded.show.externals.thetvdb is highly correlated with url and 39 other fieldsHigh correlation
_embedded.show.externals.imdb is highly correlated with id and 46 other fieldsHigh correlation
_embedded.show.image.medium is highly correlated with id and 47 other fieldsHigh correlation
_embedded.show.image.original is highly correlated with id and 47 other fieldsHigh correlation
_embedded.show.summary is highly correlated with id and 46 other fieldsHigh correlation
_embedded.show.updated is highly correlated with id and 31 other fieldsHigh correlation
_embedded.show._links.self.href is highly correlated with id and 47 other fieldsHigh correlation
_embedded.show._links.previousepisode.href is highly correlated with id and 47 other fieldsHigh correlation
_embedded.show.webChannel.country.name is highly correlated with url and 38 other fieldsHigh correlation
_embedded.show.webChannel.country.code is highly correlated with url and 38 other fieldsHigh correlation
_embedded.show.webChannel.country.timezone is highly correlated with url and 38 other fieldsHigh correlation
_embedded.show._links.nextepisode.href is highly correlated with id and 30 other fieldsHigh correlation
_embedded.show.network.id is highly correlated with id and 33 other fieldsHigh correlation
_embedded.show.network.name is highly correlated with id and 33 other fieldsHigh correlation
_embedded.show.network.country.name is highly correlated with id and 33 other fieldsHigh correlation
_embedded.show.network.country.code is highly correlated with id and 33 other fieldsHigh correlation
_embedded.show.network.country.timezone is highly correlated with id and 33 other fieldsHigh correlation
number has 4 (4.4%) missing values Missing
runtime has 7 (7.8%) missing values Missing
summary has 75 (83.3%) missing values Missing
rating.average has 82 (91.1%) missing values Missing
image.medium has 69 (76.7%) missing values Missing
image.original has 69 (76.7%) missing values Missing
_embedded.show.language has 3 (3.3%) missing values Missing
_embedded.show.runtime has 28 (31.1%) missing values Missing
_embedded.show.averageRuntime has 5 (5.6%) missing values Missing
_embedded.show.ended has 41 (45.6%) missing values Missing
_embedded.show.officialSite has 20 (22.2%) missing values Missing
_embedded.show.rating.average has 83 (92.2%) missing values Missing
_embedded.show.network has 90 (100.0%) missing values Missing
_embedded.show.webChannel.id has 1 (1.1%) missing values Missing
_embedded.show.webChannel.name has 1 (1.1%) missing values Missing
_embedded.show.webChannel.country has 90 (100.0%) missing values Missing
_embedded.show.webChannel.officialSite has 26 (28.9%) missing values Missing
_embedded.show.dvdCountry has 90 (100.0%) missing values Missing
_embedded.show.externals.tvrage has 87 (96.7%) missing values Missing
_embedded.show.externals.thetvdb has 30 (33.3%) missing values Missing
_embedded.show.externals.imdb has 52 (57.8%) missing values Missing
_embedded.show.image.medium has 2 (2.2%) missing values Missing
_embedded.show.image.original has 2 (2.2%) missing values Missing
_embedded.show.summary has 9 (10.0%) missing values Missing
image has 90 (100.0%) missing values Missing
_embedded.show.webChannel.country.name has 33 (36.7%) missing values Missing
_embedded.show.webChannel.country.code has 33 (36.7%) missing values Missing
_embedded.show.webChannel.country.timezone has 33 (36.7%) missing values Missing
_embedded.show._links.nextepisode.href has 86 (95.6%) missing values Missing
_embedded.show.network.id has 85 (94.4%) missing values Missing
_embedded.show.network.name has 85 (94.4%) missing values Missing
_embedded.show.network.country.name has 85 (94.4%) missing values Missing
_embedded.show.network.country.code has 85 (94.4%) missing values Missing
_embedded.show.network.country.timezone has 85 (94.4%) missing values Missing
_embedded.show.network.officialSite has 90 (100.0%) missing values Missing
_embedded.show.image has 90 (100.0%) missing values Missing
_embedded.show.dvdCountry.name has 89 (98.9%) missing values Missing
_embedded.show.dvdCountry.code has 89 (98.9%) missing values Missing
_embedded.show.dvdCountry.timezone has 89 (98.9%) missing values Missing
_embedded.show.webChannel has 90 (100.0%) missing values Missing
url is uniformly distributed Uniform
name is uniformly distributed Uniform
summary is uniformly distributed Uniform
image.medium is uniformly distributed Uniform
image.original is uniformly distributed Uniform
_links.self.href is uniformly distributed Uniform
_embedded.show.externals.tvrage is uniformly distributed Uniform
_embedded.show.externals.imdb is uniformly distributed Uniform
_embedded.show._links.nextepisode.href is uniformly distributed Uniform
_embedded.show.network.id is uniformly distributed Uniform
_embedded.show.network.name is uniformly distributed Uniform
_embedded.show.network.country.name is uniformly distributed Uniform
_embedded.show.network.country.code is uniformly distributed Uniform
_embedded.show.network.country.timezone is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links.self.href has unique values Unique
_embedded.show.genres is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.schedule.days is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.network is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.webChannel.country is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.dvdCountry is an unsupported type, check if it needs cleaning or further analysis Unsupported
image is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.network.officialSite is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.image is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.webChannel is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-09-05 04:43:58.649504
Analysis finished2022-09-05 04:44:29.802612
Duration31.15 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2048462.067
Minimum1945147
Maximum2386109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-09-04T23:44:29.857769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1945147
5-th percentile1967087.1
Q11988028.25
median2007042.5
Q32068321.75
95-th percentile2267651.6
Maximum2386109
Range440962
Interquartile range (IQR)80293.5

Descriptive statistics

Standard deviation99271.26779
Coefficient of variation (CV)0.04846136494
Kurtosis2.691009664
Mean2048462.067
Median Absolute Deviation (MAD)26640
Skewness1.774497095
Sum184361586
Variance9854784608
MonotonicityNot monotonic
2022-09-04T23:44:29.955768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21796141
 
1.1%
19885381
 
1.1%
19880681
 
1.1%
19963971
 
1.1%
19854761
 
1.1%
19854751
 
1.1%
19849571
 
1.1%
19849561
 
1.1%
21296331
 
1.1%
19776511
 
1.1%
Other values (80)80
88.9%
ValueCountFrequency (%)
19451471
1.1%
19459021
1.1%
19585751
1.1%
19588681
1.1%
19644961
1.1%
19702541
1.1%
19760461
1.1%
19760471
1.1%
19773311
1.1%
19774181
1.1%
ValueCountFrequency (%)
23861091
1.1%
23539171
1.1%
23539161
1.1%
23300081
1.1%
23181111
1.1%
22059791
1.1%
22059781
1.1%
21975981
1.1%
21972891
1.1%
21926261
1.1%

url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size848.0 B
https://www.tvmaze.com/episodes/2179614/kontakty-1x31-kontakty-v-telefone-eldara-dzarahova-morgenshtern-slava-marlow-basta-dana-poperecnyj
 
1
https://www.tvmaze.com/episodes/1988538/follow-my-sunshine-1x01-episode-1
 
1
https://www.tvmaze.com/episodes/1988068/forever-love-1x17-episode-17
 
1
https://www.tvmaze.com/episodes/1996397/the-expanse-aftershow-1x04-shohreh-aghdashloo-dan-nowak
 
1
https://www.tvmaze.com/episodes/1985476/you-complete-me-1x20-episode-20
 
1
Other values (85)
85 

Length

Max length138
Median length102
Mean length79.52222222
Min length63

Characters and Unicode

Total characters7157
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/2179614/kontakty-1x31-kontakty-v-telefone-eldara-dzarahova-morgenshtern-slava-marlow-basta-dana-poperecnyj
2nd rowhttps://www.tvmaze.com/episodes/1988015/muzskaa-tema-1x04-seria-4
3rd rowhttps://www.tvmaze.com/episodes/2386109/xian-feng-jian-yu-lu-1x50-episode-50
4th rowhttps://www.tvmaze.com/episodes/2095629/yi-nian-yong-heng-1x22-episode-22
5th rowhttps://www.tvmaze.com/episodes/1993657/7-days-of-romance-2x02-episode-2

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2179614/kontakty-1x31-kontakty-v-telefone-eldara-dzarahova-morgenshtern-slava-marlow-basta-dana-poperecnyj1
 
1.1%
https://www.tvmaze.com/episodes/1988538/follow-my-sunshine-1x01-episode-11
 
1.1%
https://www.tvmaze.com/episodes/1988068/forever-love-1x17-episode-171
 
1.1%
https://www.tvmaze.com/episodes/1996397/the-expanse-aftershow-1x04-shohreh-aghdashloo-dan-nowak1
 
1.1%
https://www.tvmaze.com/episodes/1985476/you-complete-me-1x20-episode-201
 
1.1%
https://www.tvmaze.com/episodes/1985475/you-complete-me-1x19-episode-191
 
1.1%
https://www.tvmaze.com/episodes/1984957/dream-detective-1x18-episode-181
 
1.1%
https://www.tvmaze.com/episodes/1984956/dream-detective-1x17-episode-171
 
1.1%
https://www.tvmaze.com/episodes/2129633/the-aam-aadmi-family-4x02-mard-ko-dard-nahi-hota1
 
1.1%
https://www.tvmaze.com/episodes/1977651/to-love-1x40-episode-401
 
1.1%
Other values (80)80
88.9%

Length

2022-09-04T23:44:30.064768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2179614/kontakty-1x31-kontakty-v-telefone-eldara-dzarahova-morgenshtern-slava-marlow-basta-dana-poperecnyj1
 
1.1%
https://www.tvmaze.com/episodes/2096301/no-turning-back-romance-1x06-61
 
1.1%
https://www.tvmaze.com/episodes/2095629/yi-nian-yong-heng-1x22-episode-221
 
1.1%
https://www.tvmaze.com/episodes/1993657/7-days-of-romance-2x02-episode-21
 
1.1%
https://www.tvmaze.com/episodes/2015712/half-fifty-1x01-episode-11
 
1.1%
https://www.tvmaze.com/episodes/2015713/half-fifty-1x02-episode-21
 
1.1%
https://www.tvmaze.com/episodes/2015714/half-fifty-1x03-episode-31
 
1.1%
https://www.tvmaze.com/episodes/2015715/half-fifty-1x04-episode-41
 
1.1%
https://www.tvmaze.com/episodes/2015716/half-fifty-1x05-episode-51
 
1.1%
https://www.tvmaze.com/episodes/2015717/half-fifty-1x06-episode-61
 
1.1%
Other values (80)80
88.9%

Most occurring characters

ValueCountFrequency (%)
e611
 
8.5%
-556
 
7.8%
s453
 
6.3%
/450
 
6.3%
t421
 
5.9%
o367
 
5.1%
a321
 
4.5%
w299
 
4.2%
i263
 
3.7%
p258
 
3.6%
Other values (30)3158
44.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4820
67.3%
Decimal Number1061
 
14.8%
Other Punctuation720
 
10.1%
Dash Punctuation556
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e611
12.7%
s453
 
9.4%
t421
 
8.7%
o367
 
7.6%
a321
 
6.7%
w299
 
6.2%
i263
 
5.5%
p258
 
5.4%
m254
 
5.3%
d210
 
4.4%
Other values (16)1363
28.3%
Decimal Number
ValueCountFrequency (%)
1227
21.4%
0174
16.4%
2158
14.9%
9102
9.6%
375
 
7.1%
769
 
6.5%
568
 
6.4%
868
 
6.4%
660
 
5.7%
460
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/450
62.5%
.180
 
25.0%
:90
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-556
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4820
67.3%
Common2337
32.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e611
12.7%
s453
 
9.4%
t421
 
8.7%
o367
 
7.6%
a321
 
6.7%
w299
 
6.2%
i263
 
5.5%
p258
 
5.4%
m254
 
5.3%
d210
 
4.4%
Other values (16)1363
28.3%
Common
ValueCountFrequency (%)
-556
23.8%
/450
19.3%
1227
9.7%
.180
 
7.7%
0174
 
7.4%
2158
 
6.8%
9102
 
4.4%
:90
 
3.9%
375
 
3.2%
769
 
3.0%
Other values (4)256
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e611
 
8.5%
-556
 
7.8%
s453
 
6.3%
/450
 
6.3%
t421
 
5.9%
o367
 
5.1%
a321
 
4.5%
w299
 
4.2%
i263
 
3.7%
p258
 
3.6%
Other values (30)3158
44.1%

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM

Distinct76
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Memory size848.0 B
Episode 3
 
3
Episode 4
 
3
Episode 18
 
3
Episode 17
 
3
Episode 22
 
2
Other values (71)
76 

Length

Max length89
Median length68
Mean length17.62222222
Min length1

Characters and Unicode

Total characters1586
Distinct characters156
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)73.3%

Sample

1st rowКОНТАКТЫ в телефоне Эльдара Джарахова: Morgenshtern, Slava Marlow, Баста, Даня Поперечный
2nd rowСерия 4
3rd rowEpisode 50
4th rowEpisode 22
5th rowEpisode 2

Common Values

ValueCountFrequency (%)
Episode 33
 
3.3%
Episode 43
 
3.3%
Episode 183
 
3.3%
Episode 173
 
3.3%
Episode 222
 
2.2%
Episode 22
 
2.2%
Episode 12
 
2.2%
Christmas Special2
 
2.2%
Episode 72
 
2.2%
Episode 82
 
2.2%
Other values (66)66
73.3%

Length

2022-09-04T23:44:30.166877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode37
 
12.7%
the8
 
2.7%
26
 
2.1%
6
 
2.1%
15
 
1.7%
of4
 
1.4%
44
 
1.4%
christmas4
 
1.4%
34
 
1.4%
to3
 
1.0%
Other values (185)210
72.2%

Most occurring characters

ValueCountFrequency (%)
201
 
12.7%
e107
 
6.7%
o79
 
5.0%
i72
 
4.5%
s66
 
4.2%
a58
 
3.7%
d56
 
3.5%
p43
 
2.7%
E41
 
2.6%
r39
 
2.5%
Other values (146)824
52.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter975
61.5%
Uppercase Letter224
 
14.1%
Space Separator201
 
12.7%
Decimal Number95
 
6.0%
Other Letter49
 
3.1%
Other Punctuation28
 
1.8%
Dash Punctuation5
 
0.3%
Close Punctuation4
 
0.3%
Open Punctuation4
 
0.3%
Math Symbol1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e107
 
11.0%
o79
 
8.1%
i72
 
7.4%
s66
 
6.8%
a58
 
5.9%
d56
 
5.7%
p43
 
4.4%
r39
 
4.0%
t38
 
3.9%
n38
 
3.9%
Other values (47)379
38.9%
Uppercase Letter
ValueCountFrequency (%)
E41
18.3%
S13
 
5.8%
К11
 
4.9%
T10
 
4.5%
D9
 
4.0%
M8
 
3.6%
W8
 
3.6%
C8
 
3.6%
B7
 
3.1%
А7
 
3.1%
Other values (36)102
45.5%
Other Letter
ValueCountFrequency (%)
7
 
14.3%
3
 
6.1%
3
 
6.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
Other values (21)22
44.9%
Decimal Number
ValueCountFrequency (%)
227
28.4%
116
16.8%
011
11.6%
311
11.6%
56
 
6.3%
86
 
6.3%
45
 
5.3%
65
 
5.3%
75
 
5.3%
93
 
3.2%
Other Punctuation
ValueCountFrequency (%)
,15
53.6%
'3
 
10.7%
:3
 
10.7%
.2
 
7.1%
/2
 
7.1%
&2
 
7.1%
?1
 
3.6%
Space Separator
ValueCountFrequency (%)
201
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5
100.0%
Close Punctuation
ValueCountFrequency (%)
)4
100.0%
Open Punctuation
ValueCountFrequency (%)
(4
100.0%
Math Symbol
ValueCountFrequency (%)
|1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin909
57.3%
Common338
 
21.3%
Cyrillic290
 
18.3%
Hangul49
 
3.1%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
а34
 
11.7%
е20
 
6.9%
р18
 
6.2%
н15
 
5.2%
о12
 
4.1%
с11
 
3.8%
и11
 
3.8%
К11
 
3.8%
т10
 
3.4%
к9
 
3.1%
Other values (44)139
47.9%
Latin
ValueCountFrequency (%)
e107
 
11.8%
o79
 
8.7%
i72
 
7.9%
s66
 
7.3%
a58
 
6.4%
d56
 
6.2%
p43
 
4.7%
E41
 
4.5%
r39
 
4.3%
t38
 
4.2%
Other values (39)310
34.1%
Hangul
ValueCountFrequency (%)
7
 
14.3%
3
 
6.1%
3
 
6.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
Other values (21)22
44.9%
Common
ValueCountFrequency (%)
201
59.5%
227
 
8.0%
116
 
4.7%
,15
 
4.4%
011
 
3.3%
311
 
3.3%
56
 
1.8%
86
 
1.8%
45
 
1.5%
-5
 
1.5%
Other values (12)35
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1244
78.4%
Cyrillic290
 
18.3%
Hangul49
 
3.1%
None3
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
201
16.2%
e107
 
8.6%
o79
 
6.4%
i72
 
5.8%
s66
 
5.3%
a58
 
4.7%
d56
 
4.5%
p43
 
3.5%
E41
 
3.3%
r39
 
3.1%
Other values (58)482
38.7%
Cyrillic
ValueCountFrequency (%)
а34
 
11.7%
е20
 
6.9%
р18
 
6.2%
н15
 
5.2%
о12
 
4.1%
с11
 
3.8%
и11
 
3.8%
К11
 
3.8%
т10
 
3.4%
к9
 
3.1%
Other values (44)139
47.9%
Hangul
ValueCountFrequency (%)
7
 
14.3%
3
 
6.1%
3
 
6.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
Other values (21)22
44.9%
None
ValueCountFrequency (%)
å1
33.3%
é1
33.3%
ó1
33.3%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.16666667
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-09-04T23:44:30.237957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile25.15
Maximum2020
Range2019
Interquartile range (IQR)2

Descriptive statistics

Standard deviation418.1158715
Coefficient of variation (CV)4.536519402
Kurtosis18.62754744
Mean92.16666667
Median Absolute Deviation (MAD)0
Skewness4.495751678
Sum8295
Variance174820.882
MonotonicityNot monotonic
2022-09-04T23:44:30.299366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
160
66.7%
48
 
8.9%
26
 
6.7%
20204
 
4.4%
73
 
3.3%
33
 
3.3%
52
 
2.2%
81
 
1.1%
181
 
1.1%
311
 
1.1%
ValueCountFrequency (%)
160
66.7%
26
 
6.7%
33
 
3.3%
48
 
8.9%
52
 
2.2%
73
 
3.3%
81
 
1.1%
141
 
1.1%
181
 
1.1%
311
 
1.1%
ValueCountFrequency (%)
20204
4.4%
311
 
1.1%
181
 
1.1%
141
 
1.1%
81
 
1.1%
73
 
3.3%
52
 
2.2%
48
8.9%
33
 
3.3%
26
6.7%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct39
Distinct (%)45.3%
Missing4
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean28.23255814
Minimum1
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-09-04T23:44:30.380661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q322.75
95-th percentile82.5
Maximum350
Range349
Interquartile range (IQR)18.75

Descriptive statistics

Standard deviation60.69434985
Coefficient of variation (CV)2.149799871
Kurtosis19.58349986
Mean28.23255814
Median Absolute Deviation (MAD)6
Skewness4.349047589
Sum2428
Variance3683.804104
MonotonicityNot monotonic
2022-09-04T23:44:30.465953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
49
 
10.0%
28
 
8.9%
16
 
6.7%
36
 
6.7%
65
 
5.6%
75
 
5.6%
203
 
3.3%
183
 
3.3%
173
 
3.3%
83
 
3.3%
Other values (29)35
38.9%
(Missing)4
 
4.4%
ValueCountFrequency (%)
16
6.7%
28
8.9%
36
6.7%
49
10.0%
52
 
2.2%
65
5.6%
75
5.6%
83
 
3.3%
91
 
1.1%
102
 
2.2%
ValueCountFrequency (%)
3501
1.1%
3161
1.1%
3151
1.1%
901
1.1%
861
1.1%
721
1.1%
671
1.1%
611
1.1%
581
1.1%
551
1.1%

type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size848.0 B
regular
86 
significant_special
 
4

Length

Max length19
Median length7
Mean length7.533333333
Min length7

Characters and Unicode

Total characters678
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular86
95.6%
significant_special4
 
4.4%

Length

2022-09-04T23:44:30.572953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:30.662127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
regular86
95.6%
significant_special4
 
4.4%

Most occurring characters

ValueCountFrequency (%)
r172
25.4%
a94
13.9%
e90
13.3%
g90
13.3%
l90
13.3%
u86
12.7%
i16
 
2.4%
s8
 
1.2%
n8
 
1.2%
c8
 
1.2%
Other values (4)16
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter674
99.4%
Connector Punctuation4
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r172
25.5%
a94
13.9%
e90
13.4%
g90
13.4%
l90
13.4%
u86
12.8%
i16
 
2.4%
s8
 
1.2%
n8
 
1.2%
c8
 
1.2%
Other values (3)12
 
1.8%
Connector Punctuation
ValueCountFrequency (%)
_4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin674
99.4%
Common4
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r172
25.5%
a94
13.9%
e90
13.4%
g90
13.4%
l90
13.4%
u86
12.8%
i16
 
2.4%
s8
 
1.2%
n8
 
1.2%
c8
 
1.2%
Other values (3)12
 
1.8%
Common
ValueCountFrequency (%)
_4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r172
25.4%
a94
13.9%
e90
13.3%
g90
13.3%
l90
13.3%
u86
12.7%
i16
 
2.4%
s8
 
1.2%
n8
 
1.2%
c8
 
1.2%
Other values (4)16
 
2.4%

airdate
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size848.0 B
2020-12-23
90 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters900
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-23
2nd row2020-12-23
3rd row2020-12-23
4th row2020-12-23
5th row2020-12-23

Common Values

ValueCountFrequency (%)
2020-12-2390
100.0%

Length

2022-09-04T23:44:30.744121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:30.825783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-2390
100.0%

Most occurring characters

ValueCountFrequency (%)
2360
40.0%
0180
20.0%
-180
20.0%
190
 
10.0%
390
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number720
80.0%
Dash Punctuation180
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2360
50.0%
0180
25.0%
190
 
12.5%
390
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2360
40.0%
0180
20.0%
-180
20.0%
190
 
10.0%
390
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2360
40.0%
0180
20.0%
-180
20.0%
190
 
10.0%
390
 
10.0%

airtime
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size848.0 B
62 
20:00
13 
06:00
 
3
12:00
 
2
10:00
 
2
Other values (7)

Length

Max length5
Median length0
Mean length1.555555556
Min length0

Characters and Unicode

Total characters140
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)6.7%

Sample

1st row12:00
2nd row12:00
3rd row10:00
4th row10:00
5th row

Common Values

ValueCountFrequency (%)
62
68.9%
20:0013
 
14.4%
06:003
 
3.3%
12:002
 
2.2%
10:002
 
2.2%
00:002
 
2.2%
18:301
 
1.1%
20:451
 
1.1%
19:001
 
1.1%
20:301
 
1.1%
Other values (2)2
 
2.2%

Length

2022-09-04T23:44:30.900088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20:0013
46.4%
06:003
 
10.7%
12:002
 
7.1%
10:002
 
7.1%
00:002
 
7.1%
18:301
 
3.6%
20:451
 
3.6%
19:001
 
3.6%
20:301
 
3.6%
21:001
 
3.6%

Most occurring characters

ValueCountFrequency (%)
076
54.3%
:28
 
20.0%
220
 
14.3%
17
 
5.0%
63
 
2.1%
32
 
1.4%
81
 
0.7%
41
 
0.7%
51
 
0.7%
91
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number112
80.0%
Other Punctuation28
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
076
67.9%
220
 
17.9%
17
 
6.2%
63
 
2.7%
32
 
1.8%
81
 
0.9%
41
 
0.9%
51
 
0.9%
91
 
0.9%
Other Punctuation
ValueCountFrequency (%)
:28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
076
54.3%
:28
 
20.0%
220
 
14.3%
17
 
5.0%
63
 
2.1%
32
 
1.4%
81
 
0.7%
41
 
0.7%
51
 
0.7%
91
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
076
54.3%
:28
 
20.0%
220
 
14.3%
17
 
5.0%
63
 
2.1%
32
 
1.4%
81
 
0.7%
41
 
0.7%
51
 
0.7%
91
 
0.7%

airstamp
Categorical

HIGH CORRELATION

Distinct19
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size848.0 B
2020-12-23T12:00:00+00:00
39 
2020-12-23T03:00:00+00:00
20 
2020-12-23T04:00:00+00:00
2020-12-23T17:00:00+00:00
2020-12-23T11:00:00+00:00
 
3
Other values (14)
18 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters2250
Distinct characters12
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)12.2%

Sample

1st row2020-12-23T00:00:00+00:00
2nd row2020-12-23T00:00:00+00:00
3rd row2020-12-23T02:00:00+00:00
4th row2020-12-23T02:00:00+00:00
5th row2020-12-23T03:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-23T12:00:00+00:0039
43.3%
2020-12-23T03:00:00+00:0020
22.2%
2020-12-23T04:00:00+00:005
 
5.6%
2020-12-23T17:00:00+00:005
 
5.6%
2020-12-23T11:00:00+00:003
 
3.3%
2020-12-23T05:00:00+00:003
 
3.3%
2020-12-23T02:00:00+00:002
 
2.2%
2020-12-23T00:00:00+00:002
 
2.2%
2020-12-23T10:00:00+00:001
 
1.1%
2020-12-23T09:30:00+00:001
 
1.1%
Other values (9)9
 
10.0%

Length

2022-09-04T23:44:30.977158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-23t12:00:00+00:0039
43.3%
2020-12-23t03:00:00+00:0020
22.2%
2020-12-23t04:00:00+00:005
 
5.6%
2020-12-23t17:00:00+00:005
 
5.6%
2020-12-23t11:00:00+00:003
 
3.3%
2020-12-23t05:00:00+00:003
 
3.3%
2020-12-23t02:00:00+00:002
 
2.2%
2020-12-23t00:00:00+00:002
 
2.2%
2020-12-23t19:00:00+00:001
 
1.1%
2020-12-24t01:00:00+00:001
 
1.1%
Other values (9)9
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0936
41.6%
2406
18.0%
:270
 
12.0%
-180
 
8.0%
1146
 
6.5%
3110
 
4.9%
T90
 
4.0%
+90
 
4.0%
48
 
0.4%
76
 
0.3%
Other values (2)8
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
72.0%
Other Punctuation270
 
12.0%
Dash Punctuation180
 
8.0%
Uppercase Letter90
 
4.0%
Math Symbol90
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0936
57.8%
2406
25.1%
1146
 
9.0%
3110
 
6.8%
48
 
0.5%
76
 
0.4%
56
 
0.4%
92
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:270
100.0%
Dash Punctuation
ValueCountFrequency (%)
-180
100.0%
Uppercase Letter
ValueCountFrequency (%)
T90
100.0%
Math Symbol
ValueCountFrequency (%)
+90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2160
96.0%
Latin90
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0936
43.3%
2406
18.8%
:270
 
12.5%
-180
 
8.3%
1146
 
6.8%
3110
 
5.1%
+90
 
4.2%
48
 
0.4%
76
 
0.3%
56
 
0.3%
Latin
ValueCountFrequency (%)
T90
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0936
41.6%
2406
18.0%
:270
 
12.0%
-180
 
8.0%
1146
 
6.5%
3110
 
4.9%
T90
 
4.0%
+90
 
4.0%
48
 
0.4%
76
 
0.3%
Other values (2)8
 
0.4%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct34
Distinct (%)41.0%
Missing7
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean32.78313253
Minimum1
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-09-04T23:44:31.053901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.1
Q115
median23
Q345
95-th percentile92.7
Maximum126
Range125
Interquartile range (IQR)30

Descriptive statistics

Standard deviation26.85736861
Coefficient of variation (CV)0.8192435114
Kurtosis4.025417362
Mean32.78313253
Median Absolute Deviation (MAD)10
Skewness1.921954605
Sum2721
Variance721.3182486
MonotonicityNot monotonic
2022-09-04T23:44:31.146960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
4516
17.8%
1512
13.3%
179
 
10.0%
305
 
5.6%
233
 
3.3%
1203
 
3.3%
203
 
3.3%
123
 
3.3%
252
 
2.2%
602
 
2.2%
Other values (24)25
27.8%
(Missing)7
 
7.8%
ValueCountFrequency (%)
11
 
1.1%
21
 
1.1%
31
 
1.1%
41
 
1.1%
51
 
1.1%
61
 
1.1%
81
 
1.1%
123
3.3%
131
 
1.1%
141
 
1.1%
ValueCountFrequency (%)
1261
 
1.1%
1203
 
3.3%
931
 
1.1%
901
 
1.1%
631
 
1.1%
602
 
2.2%
481
 
1.1%
461
 
1.1%
4516
17.8%
431
 
1.1%

summary
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct15
Distinct (%)100.0%
Missing75
Missing (%)83.3%
Memory size848.0 B
<p>Marco's grand plan shocks Earth, Mars and the Belt.</p>
 
1
<p>It's up to you to decide whether you care about the weather. Your weather girls are here to brighten up your rainy days!</p>
 
1
<p>The promised second part of the House's adventures is better in the Carpathians. This time we go to the capital of the Hutsuls - Verkhona. We will get acquainted with local traditions, talk with those who protect them, and look at a couple of museums. We will rise to the mystical summit of Spitz.</p>
 
1
<p>Kido Butai was the fleet that launched the surprise attack on the US Pacific Fleet at anchor at Pearl Harbor and followed that up with a string of victories in 1942. But how was it commanded, both as a whole and in the high and even mid level command? Today we'll look at that.</p>
 
1
<p>All hail Queen of the Earth herself on this installment of The Expanse Aftershow! Shohreh Aghdashloo aka Chrisjen Avasarala and EP &amp; Writer Dan Nowak join Wes Chatham and Ty Franck to discuss what convinced Shohreh to join The Expanse, Amos' survival instincts, and Under Siege (1992) references.</p>
 
1
Other values (10)
10 

Length

Max length434
Median length284
Mean length265.1333333
Min length58

Characters and Unicode

Total characters3977
Distinct characters70
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)100.0%

Sample

1st row<p>Marco's grand plan shocks Earth, Mars and the Belt.</p>
2nd row<p>It's up to you to decide whether you care about the weather. Your weather girls are here to brighten up your rainy days!</p>
3rd row<p>The promised second part of the House's adventures is better in the Carpathians. This time we go to the capital of the Hutsuls - Verkhona. We will get acquainted with local traditions, talk with those who protect them, and look at a couple of museums. We will rise to the mystical summit of Spitz.</p>
4th row<p>Kido Butai was the fleet that launched the surprise attack on the US Pacific Fleet at anchor at Pearl Harbor and followed that up with a string of victories in 1942. But how was it commanded, both as a whole and in the high and even mid level command? Today we'll look at that.</p>
5th row<p>All hail Queen of the Earth herself on this installment of The Expanse Aftershow! Shohreh Aghdashloo aka Chrisjen Avasarala and EP &amp; Writer Dan Nowak join Wes Chatham and Ty Franck to discuss what convinced Shohreh to join The Expanse, Amos' survival instincts, and Under Siege (1992) references.</p>

Common Values

ValueCountFrequency (%)
<p>Marco's grand plan shocks Earth, Mars and the Belt.</p>1
 
1.1%
<p>It's up to you to decide whether you care about the weather. Your weather girls are here to brighten up your rainy days!</p>1
 
1.1%
<p>The promised second part of the House's adventures is better in the Carpathians. This time we go to the capital of the Hutsuls - Verkhona. We will get acquainted with local traditions, talk with those who protect them, and look at a couple of museums. We will rise to the mystical summit of Spitz.</p>1
 
1.1%
<p>Kido Butai was the fleet that launched the surprise attack on the US Pacific Fleet at anchor at Pearl Harbor and followed that up with a string of victories in 1942. But how was it commanded, both as a whole and in the high and even mid level command? Today we'll look at that.</p>1
 
1.1%
<p>All hail Queen of the Earth herself on this installment of The Expanse Aftershow! Shohreh Aghdashloo aka Chrisjen Avasarala and EP &amp; Writer Dan Nowak join Wes Chatham and Ty Franck to discuss what convinced Shohreh to join The Expanse, Amos' survival instincts, and Under Siege (1992) references.</p>1
 
1.1%
<p>Welcome to the SEASON 2 FINALE of our spooky and FESTIVE show- Too Many Spirits! Join us as we read your submitted holiday ghost stories and enjoy cocktails prepared by freshman bartender, Steven Lim.</p>1
 
1.1%
<p>Following his opponent's curveball COVID diagnosis, Ryan decides to take a trip back to his hometown of Victorville, Calif., where he first fell in love with boxing. As he heads into the biggest fight of his young career, Ryan is driven by a pressure he places upon himself to not only exceed the expectations of his family and friends, but to provide opportunities for the ones he loves and a life they never dreamed.</p>1
 
1.1%
<p>James's night is not going as planned. On top of it all, Dale conceals a small but significant detail from him.</p>1
 
1.1%
<p>Stephanie tries to recover, and struggles with her reality. Cameron Sr. fights to unify his fractured family while Cameron Jr. follows his ambitions. Brittany senses a rift within her love triangle.</p>1
 
1.1%
<p>In the glory days of Japan's over-the-top MMA events, Kazushi Sakuraba embodied the samurai spirit of PRIDE Fighting Championship. Athletes, personalities and "The Gracie Hunter" himself tell how a pro wrestler became a superstar in the PRIDE ring.</p>1
 
1.1%
Other values (5)5
 
5.6%
(Missing)75
83.3%

Length

2022-09-04T23:44:31.235087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the37
 
5.5%
and25
 
3.7%
to24
 
3.6%
of18
 
2.7%
a16
 
2.4%
his9
 
1.3%
christmas8
 
1.2%
with8
 
1.2%
is8
 
1.2%
he7
 
1.0%
Other values (393)511
76.2%

Most occurring characters

ValueCountFrequency (%)
656
16.5%
e361
 
9.1%
t283
 
7.1%
a253
 
6.4%
i216
 
5.4%
s212
 
5.3%
o205
 
5.2%
n189
 
4.8%
h186
 
4.7%
r182
 
4.6%
Other values (60)1234
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2968
74.6%
Space Separator656
 
16.5%
Uppercase Letter163
 
4.1%
Other Punctuation111
 
2.8%
Math Symbol60
 
1.5%
Decimal Number9
 
0.2%
Dash Punctuation6
 
0.2%
Open Punctuation2
 
0.1%
Close Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e361
12.2%
t283
 
9.5%
a253
 
8.5%
i216
 
7.3%
s212
 
7.1%
o205
 
6.9%
n189
 
6.4%
h186
 
6.3%
r182
 
6.1%
l135
 
4.5%
Other values (16)746
25.1%
Uppercase Letter
ValueCountFrequency (%)
C21
12.9%
S17
 
10.4%
A14
 
8.6%
I11
 
6.7%
E11
 
6.7%
T9
 
5.5%
M8
 
4.9%
J7
 
4.3%
B7
 
4.3%
G6
 
3.7%
Other values (14)52
31.9%
Other Punctuation
ValueCountFrequency (%)
.35
31.5%
,34
30.6%
'17
15.3%
/15
13.5%
!3
 
2.7%
"2
 
1.8%
?2
 
1.8%
&1
 
0.9%
;1
 
0.9%
:1
 
0.9%
Decimal Number
ValueCountFrequency (%)
23
33.3%
93
33.3%
12
22.2%
41
 
11.1%
Math Symbol
ValueCountFrequency (%)
>30
50.0%
<30
50.0%
Space Separator
ValueCountFrequency (%)
656
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3131
78.7%
Common846
 
21.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e361
 
11.5%
t283
 
9.0%
a253
 
8.1%
i216
 
6.9%
s212
 
6.8%
o205
 
6.5%
n189
 
6.0%
h186
 
5.9%
r182
 
5.8%
l135
 
4.3%
Other values (40)909
29.0%
Common
ValueCountFrequency (%)
656
77.5%
.35
 
4.1%
,34
 
4.0%
>30
 
3.5%
<30
 
3.5%
'17
 
2.0%
/15
 
1.8%
-6
 
0.7%
!3
 
0.4%
23
 
0.4%
Other values (10)17
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
656
16.5%
e361
 
9.1%
t283
 
7.1%
a253
 
6.4%
i216
 
5.4%
s212
 
5.3%
o205
 
5.2%
n189
 
4.8%
h186
 
4.7%
r182
 
4.6%
Other values (60)1234
31.0%

rating.average
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)87.5%
Missing82
Missing (%)91.1%
Infinite0
Infinite (%)0.0%
Mean7.7375
Minimum3.7
Maximum9.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-09-04T23:44:31.311087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile5.03
Q17.875
median8.25
Q38.5
95-th percentile8.955
Maximum9.2
Range5.5
Interquartile range (IQR)0.625

Descriptive statistics

Standard deviation1.701207554
Coefficient of variation (CV)0.2198652736
Kurtosis6.198865624
Mean7.7375
Median Absolute Deviation (MAD)0.25
Skewness-2.380065264
Sum61.9
Variance2.894107143
MonotonicityNot monotonic
2022-09-04T23:44:31.374394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
8.52
 
2.2%
9.21
 
1.1%
81
 
1.1%
7.51
 
1.1%
8.21
 
1.1%
8.31
 
1.1%
3.71
 
1.1%
(Missing)82
91.1%
ValueCountFrequency (%)
3.71
1.1%
7.51
1.1%
81
1.1%
8.21
1.1%
8.31
1.1%
8.52
2.2%
9.21
1.1%
ValueCountFrequency (%)
9.21
1.1%
8.52
2.2%
8.31
1.1%
8.21
1.1%
81
1.1%
7.51
1.1%
3.71
1.1%

image.medium
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct21
Distinct (%)100.0%
Missing69
Missing (%)76.7%
Memory size848.0 B
https://static.tvmaze.com/uploads/images/medium_landscape/360/901424.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/290/726476.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/290/726151.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/288/720523.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/289/723175.jpg
 
1
Other values (16)
16 

Length

Max length72
Median length72
Mean length72
Min length72

Characters and Unicode

Total characters1512
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_landscape/360/901424.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/290/726357.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/296/740341.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/296/740342.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/290/726046.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_landscape/360/901424.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/medium_landscape/290/726476.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/medium_landscape/290/726151.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/medium_landscape/288/720523.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/medium_landscape/289/723175.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/medium_landscape/292/732258.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/medium_landscape/290/726760.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/medium_landscape/317/794075.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/medium_landscape/290/725930.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/medium_landscape/375/937753.jpg1
 
1.1%
Other values (11)11
 
12.2%
(Missing)69
76.7%

Length

2022-09-04T23:44:31.454429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_landscape/360/901424.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/medium_landscape/290/726237.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/medium_landscape/290/726357.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/medium_landscape/296/740341.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/medium_landscape/296/740342.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/medium_landscape/290/726046.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/medium_landscape/293/734760.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/medium_landscape/290/725745.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/medium_landscape/294/736401.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/medium_landscape/291/728566.jpg1
 
4.8%
Other values (11)11
52.4%

Most occurring characters

ValueCountFrequency (%)
/147
 
9.7%
a126
 
8.3%
m105
 
6.9%
s105
 
6.9%
t105
 
6.9%
p84
 
5.6%
e84
 
5.6%
.63
 
4.2%
d63
 
4.2%
c63
 
4.2%
Other values (22)567
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1071
70.8%
Other Punctuation231
 
15.3%
Decimal Number189
 
12.5%
Connector Punctuation21
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a126
11.8%
m105
9.8%
s105
9.8%
t105
9.8%
p84
 
7.8%
e84
 
7.8%
d63
 
5.9%
c63
 
5.9%
i63
 
5.9%
g42
 
3.9%
Other values (8)231
21.6%
Decimal Number
ValueCountFrequency (%)
237
19.6%
732
16.9%
024
12.7%
922
11.6%
318
9.5%
616
8.5%
413
 
6.9%
513
 
6.9%
18
 
4.2%
86
 
3.2%
Other Punctuation
ValueCountFrequency (%)
/147
63.6%
.63
27.3%
:21
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1071
70.8%
Common441
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a126
11.8%
m105
9.8%
s105
9.8%
t105
9.8%
p84
 
7.8%
e84
 
7.8%
d63
 
5.9%
c63
 
5.9%
i63
 
5.9%
g42
 
3.9%
Other values (8)231
21.6%
Common
ValueCountFrequency (%)
/147
33.3%
.63
14.3%
237
 
8.4%
732
 
7.3%
024
 
5.4%
922
 
5.0%
_21
 
4.8%
:21
 
4.8%
318
 
4.1%
616
 
3.6%
Other values (4)40
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/147
 
9.7%
a126
 
8.3%
m105
 
6.9%
s105
 
6.9%
t105
 
6.9%
p84
 
5.6%
e84
 
5.6%
.63
 
4.2%
d63
 
4.2%
c63
 
4.2%
Other values (22)567
37.5%

image.original
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct21
Distinct (%)100.0%
Missing69
Missing (%)76.7%
Memory size848.0 B
https://static.tvmaze.com/uploads/images/original_untouched/360/901424.jpg
 
1
https://static.tvmaze.com/uploads/images/original_untouched/290/726476.jpg
 
1
https://static.tvmaze.com/uploads/images/original_untouched/290/726151.jpg
 
1
https://static.tvmaze.com/uploads/images/original_untouched/288/720523.jpg
 
1
https://static.tvmaze.com/uploads/images/original_untouched/289/723175.jpg
 
1
Other values (16)
16 

Length

Max length74
Median length74
Mean length74
Min length74

Characters and Unicode

Total characters1554
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/360/901424.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/290/726357.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/296/740341.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/296/740342.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/290/726046.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/360/901424.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/original_untouched/290/726476.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/original_untouched/290/726151.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/original_untouched/288/720523.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/original_untouched/289/723175.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/original_untouched/292/732258.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/original_untouched/290/726760.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/original_untouched/317/794075.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/original_untouched/290/725930.jpg1
 
1.1%
https://static.tvmaze.com/uploads/images/original_untouched/375/937753.jpg1
 
1.1%
Other values (11)11
 
12.2%
(Missing)69
76.7%

Length

2022-09-04T23:44:31.677902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/360/901424.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/original_untouched/290/726237.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/original_untouched/290/726357.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/original_untouched/296/740341.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/original_untouched/296/740342.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/original_untouched/290/726046.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/original_untouched/293/734760.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/original_untouched/290/725745.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/original_untouched/294/736401.jpg1
 
4.8%
https://static.tvmaze.com/uploads/images/original_untouched/291/728566.jpg1
 
4.8%
Other values (11)11
52.4%

Most occurring characters

ValueCountFrequency (%)
/147
 
9.5%
t126
 
8.1%
a105
 
6.8%
s84
 
5.4%
i84
 
5.4%
o84
 
5.4%
p63
 
4.1%
c63
 
4.1%
.63
 
4.1%
g63
 
4.1%
Other values (23)672
43.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1113
71.6%
Other Punctuation231
 
14.9%
Decimal Number189
 
12.2%
Connector Punctuation21
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t126
 
11.3%
a105
 
9.4%
s84
 
7.5%
i84
 
7.5%
o84
 
7.5%
p63
 
5.7%
c63
 
5.7%
g63
 
5.7%
m63
 
5.7%
e63
 
5.7%
Other values (9)315
28.3%
Decimal Number
ValueCountFrequency (%)
237
19.6%
732
16.9%
024
12.7%
922
11.6%
318
9.5%
616
8.5%
413
 
6.9%
513
 
6.9%
18
 
4.2%
86
 
3.2%
Other Punctuation
ValueCountFrequency (%)
/147
63.6%
.63
27.3%
:21
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1113
71.6%
Common441
 
28.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t126
 
11.3%
a105
 
9.4%
s84
 
7.5%
i84
 
7.5%
o84
 
7.5%
p63
 
5.7%
c63
 
5.7%
g63
 
5.7%
m63
 
5.7%
e63
 
5.7%
Other values (9)315
28.3%
Common
ValueCountFrequency (%)
/147
33.3%
.63
14.3%
237
 
8.4%
732
 
7.3%
024
 
5.4%
922
 
5.0%
:21
 
4.8%
_21
 
4.8%
318
 
4.1%
616
 
3.6%
Other values (4)40
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1554
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/147
 
9.5%
t126
 
8.1%
a105
 
6.8%
s84
 
5.4%
i84
 
5.4%
o84
 
5.4%
p63
 
4.1%
c63
 
4.1%
.63
 
4.1%
g63
 
4.1%
Other values (23)672
43.2%

_links.self.href
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size848.0 B
https://api.tvmaze.com/episodes/2179614
 
1
https://api.tvmaze.com/episodes/1988538
 
1
https://api.tvmaze.com/episodes/1988068
 
1
https://api.tvmaze.com/episodes/1996397
 
1
https://api.tvmaze.com/episodes/1985476
 
1
Other values (85)
85 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters3510
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/2179614
2nd rowhttps://api.tvmaze.com/episodes/1988015
3rd rowhttps://api.tvmaze.com/episodes/2386109
4th rowhttps://api.tvmaze.com/episodes/2095629
5th rowhttps://api.tvmaze.com/episodes/1993657

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/21796141
 
1.1%
https://api.tvmaze.com/episodes/19885381
 
1.1%
https://api.tvmaze.com/episodes/19880681
 
1.1%
https://api.tvmaze.com/episodes/19963971
 
1.1%
https://api.tvmaze.com/episodes/19854761
 
1.1%
https://api.tvmaze.com/episodes/19854751
 
1.1%
https://api.tvmaze.com/episodes/19849571
 
1.1%
https://api.tvmaze.com/episodes/19849561
 
1.1%
https://api.tvmaze.com/episodes/21296331
 
1.1%
https://api.tvmaze.com/episodes/19776511
 
1.1%
Other values (80)80
88.9%

Length

2022-09-04T23:44:31.746667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/21796141
 
1.1%
https://api.tvmaze.com/episodes/20963011
 
1.1%
https://api.tvmaze.com/episodes/20956291
 
1.1%
https://api.tvmaze.com/episodes/19936571
 
1.1%
https://api.tvmaze.com/episodes/20157121
 
1.1%
https://api.tvmaze.com/episodes/20157131
 
1.1%
https://api.tvmaze.com/episodes/20157141
 
1.1%
https://api.tvmaze.com/episodes/20157151
 
1.1%
https://api.tvmaze.com/episodes/20157161
 
1.1%
https://api.tvmaze.com/episodes/20157171
 
1.1%
Other values (80)80
88.9%

Most occurring characters

ValueCountFrequency (%)
/360
 
10.3%
p270
 
7.7%
s270
 
7.7%
e270
 
7.7%
t270
 
7.7%
o180
 
5.1%
a180
 
5.1%
i180
 
5.1%
.180
 
5.1%
m180
 
5.1%
Other values (16)1170
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2250
64.1%
Other Punctuation630
 
17.9%
Decimal Number630
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p270
12.0%
s270
12.0%
e270
12.0%
t270
12.0%
o180
8.0%
a180
8.0%
i180
8.0%
m180
8.0%
h90
 
4.0%
d90
 
4.0%
Other values (3)270
12.0%
Decimal Number
ValueCountFrequency (%)
1112
17.8%
996
15.2%
280
12.7%
071
11.3%
852
8.3%
750
7.9%
549
7.8%
646
7.3%
340
 
6.3%
434
 
5.4%
Other Punctuation
ValueCountFrequency (%)
/360
57.1%
.180
28.6%
:90
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2250
64.1%
Common1260
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/360
28.6%
.180
14.3%
1112
 
8.9%
996
 
7.6%
:90
 
7.1%
280
 
6.3%
071
 
5.6%
852
 
4.1%
750
 
4.0%
549
 
3.9%
Other values (3)120
 
9.5%
Latin
ValueCountFrequency (%)
p270
12.0%
s270
12.0%
e270
12.0%
t270
12.0%
o180
8.0%
a180
8.0%
i180
8.0%
m180
8.0%
h90
 
4.0%
d90
 
4.0%
Other values (3)270
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/360
 
10.3%
p270
 
7.7%
s270
 
7.7%
e270
 
7.7%
t270
 
7.7%
o180
 
5.1%
a180
 
5.1%
i180
 
5.1%
.180
 
5.1%
m180
 
5.1%
Other values (16)1170
33.3%

_embedded.show.id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct65
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47846.68889
Minimum1825
Maximum62764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-09-04T23:44:31.830207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1825
5-th percentile18168.25
Q148448
median52421
Q353458
95-th percentile58689
Maximum62764
Range60939
Interquartile range (IQR)5010

Descriptive statistics

Standard deviation12874.92476
Coefficient of variation (CV)0.2690870582
Kurtosis4.404249198
Mean47846.68889
Median Absolute Deviation (MAD)2243
Skewness-2.142441605
Sum4306202
Variance165763687.6
MonotonicityNot monotonic
2022-09-04T23:44:31.927754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
531018
 
8.9%
546648
 
8.9%
217352
 
2.2%
586892
 
2.2%
524002
 
2.2%
524212
 
2.2%
525242
 
2.2%
527432
 
2.2%
152502
 
2.2%
521042
 
2.2%
Other values (55)58
64.4%
ValueCountFrequency (%)
18251
1.1%
22661
1.1%
25041
1.1%
152502
2.2%
217352
2.2%
249631
1.1%
283461
1.1%
306061
1.1%
326801
1.1%
339441
1.1%
ValueCountFrequency (%)
627642
2.2%
621271
1.1%
617551
1.1%
586892
2.2%
584261
1.1%
583671
1.1%
574781
1.1%
567831
1.1%
567461
1.1%
565311
1.1%

_embedded.show.url
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct65
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Memory size848.0 B
https://www.tvmaze.com/shows/53101/half-fifty
https://www.tvmaze.com/shows/54664/100-era
https://www.tvmaze.com/shows/21735/jerks
 
2
https://www.tvmaze.com/shows/58689/my-supernatural-power
 
2
https://www.tvmaze.com/shows/52400/dream-detective
 
2
Other values (60)
68 

Length

Max length69
Median length60.5
Mean length49.82222222
Min length40

Characters and Unicode

Total characters4484
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)57.8%

Sample

1st rowhttps://www.tvmaze.com/shows/49630/kontakty
2nd rowhttps://www.tvmaze.com/shows/52520/muzskaa-tema
3rd rowhttps://www.tvmaze.com/shows/49206/xian-feng-jian-yu-lu
4th rowhttps://www.tvmaze.com/shows/49652/yi-nian-yong-heng
5th rowhttps://www.tvmaze.com/shows/44276/7-days-of-romance

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/53101/half-fifty8
 
8.9%
https://www.tvmaze.com/shows/54664/100-era8
 
8.9%
https://www.tvmaze.com/shows/21735/jerks2
 
2.2%
https://www.tvmaze.com/shows/58689/my-supernatural-power2
 
2.2%
https://www.tvmaze.com/shows/52400/dream-detective2
 
2.2%
https://www.tvmaze.com/shows/52421/you-complete-me2
 
2.2%
https://www.tvmaze.com/shows/52524/forever-love2
 
2.2%
https://www.tvmaze.com/shows/52743/the-penalty-zone2
 
2.2%
https://www.tvmaze.com/shows/15250/the-young-turks2
 
2.2%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love2
 
2.2%
Other values (55)58
64.4%

Length

2022-09-04T23:44:32.022754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/53101/half-fifty8
 
8.9%
https://www.tvmaze.com/shows/54664/100-era8
 
8.9%
https://www.tvmaze.com/shows/52743/the-penalty-zone2
 
2.2%
https://www.tvmaze.com/shows/52159/to-love2
 
2.2%
https://www.tvmaze.com/shows/53458/verdens-minste-kommentatorboks2
 
2.2%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love2
 
2.2%
https://www.tvmaze.com/shows/15250/the-young-turks2
 
2.2%
https://www.tvmaze.com/shows/62764/300-year-old-class-of-20202
 
2.2%
https://www.tvmaze.com/shows/52524/forever-love2
 
2.2%
https://www.tvmaze.com/shows/52421/you-complete-me2
 
2.2%
Other values (55)58
64.4%

Most occurring characters

ValueCountFrequency (%)
/450
 
10.0%
w378
 
8.4%
t354
 
7.9%
s335
 
7.5%
o263
 
5.9%
e229
 
5.1%
m224
 
5.0%
h222
 
5.0%
a188
 
4.2%
.180
 
4.0%
Other values (30)1661
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3105
69.2%
Other Punctuation720
 
16.1%
Decimal Number494
 
11.0%
Dash Punctuation165
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w378
12.2%
t354
11.4%
s335
10.8%
o263
 
8.5%
e229
 
7.4%
m224
 
7.2%
h222
 
7.1%
a188
 
6.1%
c111
 
3.6%
v109
 
3.5%
Other values (16)692
22.3%
Decimal Number
ValueCountFrequency (%)
584
17.0%
467
13.6%
261
12.3%
060
12.1%
156
11.3%
649
9.9%
736
7.3%
335
7.1%
827
 
5.5%
919
 
3.8%
Other Punctuation
ValueCountFrequency (%)
/450
62.5%
.180
 
25.0%
:90
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3105
69.2%
Common1379
30.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
w378
12.2%
t354
11.4%
s335
10.8%
o263
 
8.5%
e229
 
7.4%
m224
 
7.2%
h222
 
7.1%
a188
 
6.1%
c111
 
3.6%
v109
 
3.5%
Other values (16)692
22.3%
Common
ValueCountFrequency (%)
/450
32.6%
.180
 
13.1%
-165
 
12.0%
:90
 
6.5%
584
 
6.1%
467
 
4.9%
261
 
4.4%
060
 
4.4%
156
 
4.1%
649
 
3.6%
Other values (4)117
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/450
 
10.0%
w378
 
8.4%
t354
 
7.9%
s335
 
7.5%
o263
 
5.9%
e229
 
5.1%
m224
 
5.0%
h222
 
5.0%
a188
 
4.2%
.180
 
4.0%
Other values (30)1661
37.0%

_embedded.show.name
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct65
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Memory size848.0 B
Half-Fifty
100% Era
jerks.
 
2
My Supernatural Power
 
2
Dream Detective
 
2
Other values (60)
68 

Length

Max length35
Median length26
Mean length15.07777778
Min length5

Characters and Unicode

Total characters1357
Distinct characters95
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)57.8%

Sample

1st rowКонтакты
2nd rowМужская тема
3rd rowXian Feng Jian Yu Lu
4th rowYi Nian Yong Heng
5th row7 Days of Romance

Common Values

ValueCountFrequency (%)
Half-Fifty8
 
8.9%
100% Era8
 
8.9%
jerks.2
 
2.2%
My Supernatural Power2
 
2.2%
Dream Detective2
 
2.2%
You Complete Me2
 
2.2%
Forever Love2
 
2.2%
The Penalty Zone2
 
2.2%
The Young Turks2
 
2.2%
Twisted Fate of Love2
 
2.2%
Other values (55)58
64.4%

Length

2022-09-04T23:44:32.108758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of9
 
3.7%
the9
 
3.7%
half-fifty8
 
3.3%
era8
 
3.3%
love8
 
3.3%
1008
 
3.3%
my4
 
1.6%
you3
 
1.2%
me3
 
1.2%
yi3
 
1.2%
Other values (153)182
74.3%

Most occurring characters

ValueCountFrequency (%)
155
 
11.4%
e120
 
8.8%
a78
 
5.7%
o71
 
5.2%
r60
 
4.4%
i60
 
4.4%
n58
 
4.3%
t55
 
4.1%
s44
 
3.2%
l36
 
2.7%
Other values (85)620
45.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter912
67.2%
Uppercase Letter214
 
15.8%
Space Separator155
 
11.4%
Decimal Number47
 
3.5%
Other Punctuation19
 
1.4%
Dash Punctuation10
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e120
 
13.2%
a78
 
8.6%
o71
 
7.8%
r60
 
6.6%
i60
 
6.6%
n58
 
6.4%
t55
 
6.0%
s44
 
4.8%
l36
 
3.9%
h29
 
3.2%
Other values (40)301
33.0%
Uppercase Letter
ValueCountFrequency (%)
T21
 
9.8%
F18
 
8.4%
M18
 
8.4%
Y14
 
6.5%
S14
 
6.5%
L13
 
6.1%
E13
 
6.1%
H12
 
5.6%
D10
 
4.7%
A9
 
4.2%
Other values (22)72
33.6%
Decimal Number
ValueCountFrequency (%)
027
57.4%
19
 
19.1%
26
 
12.8%
33
 
6.4%
71
 
2.1%
51
 
2.1%
Other Punctuation
ValueCountFrequency (%)
%8
42.1%
:6
31.6%
.3
 
15.8%
'1
 
5.3%
,1
 
5.3%
Space Separator
ValueCountFrequency (%)
155
100.0%
Dash Punctuation
ValueCountFrequency (%)
-10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1035
76.3%
Common231
 
17.0%
Cyrillic91
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e120
 
11.6%
a78
 
7.5%
o71
 
6.9%
r60
 
5.8%
i60
 
5.8%
n58
 
5.6%
t55
 
5.3%
s44
 
4.3%
l36
 
3.5%
h29
 
2.8%
Other values (41)424
41.0%
Cyrillic
ValueCountFrequency (%)
а10
 
11.0%
т8
 
8.8%
к7
 
7.7%
о7
 
7.7%
р6
 
6.6%
е6
 
6.6%
и4
 
4.4%
н4
 
4.4%
с4
 
4.4%
з3
 
3.3%
Other values (21)32
35.2%
Common
ValueCountFrequency (%)
155
67.1%
027
 
11.7%
-10
 
4.3%
19
 
3.9%
%8
 
3.5%
:6
 
2.6%
26
 
2.6%
.3
 
1.3%
33
 
1.3%
'1
 
0.4%
Other values (3)3
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1265
93.2%
Cyrillic91
 
6.7%
None1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
155
 
12.3%
e120
 
9.5%
a78
 
6.2%
o71
 
5.6%
r60
 
4.7%
i60
 
4.7%
n58
 
4.6%
t55
 
4.3%
s44
 
3.5%
l36
 
2.8%
Other values (53)528
41.7%
Cyrillic
ValueCountFrequency (%)
а10
 
11.0%
т8
 
8.8%
к7
 
7.7%
о7
 
7.7%
р6
 
6.6%
е6
 
6.6%
и4
 
4.4%
н4
 
4.4%
с4
 
4.4%
з3
 
3.3%
Other values (21)32
35.2%
None
ValueCountFrequency (%)
ø1
100.0%

_embedded.show.type
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size848.0 B
Scripted
55 
Talk Show
Animation
Reality
Documentary
 
4
Other values (4)

Length

Max length11
Median length8
Mean length8.122222222
Min length4

Characters and Unicode

Total characters731
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGame Show
2nd rowTalk Show
3rd rowAnimation
4th rowAnimation
5th rowScripted

Common Values

ValueCountFrequency (%)
Scripted55
61.1%
Talk Show9
 
10.0%
Animation7
 
7.8%
Reality6
 
6.7%
Documentary4
 
4.4%
Game Show3
 
3.3%
Sports2
 
2.2%
News2
 
2.2%
Variety2
 
2.2%

Length

2022-09-04T23:44:32.189757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:32.274757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
scripted55
53.9%
show12
 
11.8%
talk9
 
8.8%
animation7
 
6.9%
reality6
 
5.9%
documentary4
 
3.9%
game3
 
2.9%
sports2
 
2.0%
news2
 
2.0%
variety2
 
2.0%

Most occurring characters

ValueCountFrequency (%)
i77
10.5%
t76
10.4%
e72
9.8%
S69
9.4%
r63
8.6%
c59
8.1%
p57
 
7.8%
d55
 
7.5%
a31
 
4.2%
o25
 
3.4%
Other values (17)147
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter617
84.4%
Uppercase Letter102
 
14.0%
Space Separator12
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i77
12.5%
t76
12.3%
e72
11.7%
r63
10.2%
c59
9.6%
p57
9.2%
d55
8.9%
a31
 
5.0%
o25
 
4.1%
n18
 
2.9%
Other values (8)84
13.6%
Uppercase Letter
ValueCountFrequency (%)
S69
67.6%
T9
 
8.8%
A7
 
6.9%
R6
 
5.9%
D4
 
3.9%
G3
 
2.9%
N2
 
2.0%
V2
 
2.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin719
98.4%
Common12
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i77
10.7%
t76
10.6%
e72
10.0%
S69
9.6%
r63
8.8%
c59
8.2%
p57
7.9%
d55
7.6%
a31
 
4.3%
o25
 
3.5%
Other values (16)135
18.8%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i77
10.5%
t76
10.4%
e72
9.8%
S69
9.4%
r63
8.6%
c59
8.1%
p57
 
7.8%
d55
 
7.5%
a31
 
4.2%
o25
 
3.4%
Other values (17)147
20.1%

_embedded.show.language
Categorical

HIGH CORRELATION
MISSING

Distinct17
Distinct (%)19.5%
Missing3
Missing (%)3.3%
Memory size848.0 B
Korean
21 
Chinese
20 
English
19 
Russian
Norwegian
Other values (12)
17 

Length

Max length10
Median length7
Mean length6.873563218
Min length4

Characters and Unicode

Total characters598
Distinct characters34
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)8.0%

Sample

1st rowRussian
2nd rowRussian
3rd rowChinese
4th rowChinese
5th rowKorean

Common Values

ValueCountFrequency (%)
Korean21
23.3%
Chinese20
22.2%
English19
21.1%
Russian5
 
5.6%
Norwegian5
 
5.6%
Ukrainian2
 
2.2%
Japanese2
 
2.2%
Arabic2
 
2.2%
German2
 
2.2%
Tagalog2
 
2.2%
Other values (7)7
 
7.8%
(Missing)3
 
3.3%

Length

2022-09-04T23:44:32.360766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
korean21
24.1%
chinese20
23.0%
english19
21.8%
russian5
 
5.7%
norwegian5
 
5.7%
arabic2
 
2.3%
tagalog2
 
2.3%
german2
 
2.3%
japanese2
 
2.3%
ukrainian2
 
2.3%
Other values (7)7
 
8.0%

Most occurring characters

ValueCountFrequency (%)
n83
13.9%
e76
12.7%
i59
9.9%
a54
9.0%
s53
8.9%
h44
 
7.4%
r34
 
5.7%
g29
 
4.8%
o28
 
4.7%
K22
 
3.7%
Other values (24)116
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter511
85.5%
Uppercase Letter87
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n83
16.2%
e76
14.9%
i59
11.5%
a54
10.6%
s53
10.4%
h44
8.6%
r34
6.7%
g29
 
5.7%
o28
 
5.5%
l21
 
4.1%
Other values (9)30
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
K22
25.3%
C20
23.0%
E19
21.8%
R5
 
5.7%
N5
 
5.7%
T3
 
3.4%
G2
 
2.3%
U2
 
2.3%
A2
 
2.3%
J2
 
2.3%
Other values (5)5
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin598
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n83
13.9%
e76
12.7%
i59
9.9%
a54
9.0%
s53
8.9%
h44
 
7.4%
r34
 
5.7%
g29
 
4.8%
o28
 
4.7%
K22
 
3.7%
Other values (24)116
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n83
13.9%
e76
12.7%
i59
9.9%
a54
9.0%
s53
8.9%
h44
 
7.4%
r34
 
5.7%
g29
 
4.8%
o28
 
4.7%
K22
 
3.7%
Other values (24)116
19.4%

_embedded.show.genres
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size848.0 B

_embedded.show.status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size848.0 B
Ended
49 
Running
34 
To Be Determined

Length

Max length16
Median length5
Mean length6.611111111
Min length5

Characters and Unicode

Total characters595
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowEnded
3rd rowRunning
4th rowRunning
5th rowEnded

Common Values

ValueCountFrequency (%)
Ended49
54.4%
Running34
37.8%
To Be Determined7
 
7.8%

Length

2022-09-04T23:44:32.434602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:32.509682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ended49
47.1%
running34
32.7%
to7
 
6.7%
be7
 
6.7%
determined7
 
6.7%

Most occurring characters

ValueCountFrequency (%)
n158
26.6%
d105
17.6%
e77
12.9%
E49
 
8.2%
i41
 
6.9%
R34
 
5.7%
u34
 
5.7%
g34
 
5.7%
14
 
2.4%
T7
 
1.2%
Other values (6)42
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter477
80.2%
Uppercase Letter104
 
17.5%
Space Separator14
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n158
33.1%
d105
22.0%
e77
16.1%
i41
 
8.6%
u34
 
7.1%
g34
 
7.1%
o7
 
1.5%
t7
 
1.5%
r7
 
1.5%
m7
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
E49
47.1%
R34
32.7%
T7
 
6.7%
B7
 
6.7%
D7
 
6.7%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin581
97.6%
Common14
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n158
27.2%
d105
18.1%
e77
13.3%
E49
 
8.4%
i41
 
7.1%
R34
 
5.9%
u34
 
5.9%
g34
 
5.9%
T7
 
1.2%
o7
 
1.2%
Other values (5)35
 
6.0%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII595
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n158
26.6%
d105
17.6%
e77
12.9%
E49
 
8.2%
i41
 
6.9%
R34
 
5.7%
u34
 
5.7%
g34
 
5.7%
14
 
2.4%
T7
 
1.2%
Other values (6)42
 
7.1%

_embedded.show.runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct20
Distinct (%)32.3%
Missing28
Missing (%)31.1%
Infinite0
Infinite (%)0.0%
Mean37.11290323
Minimum2
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-09-04T23:44:32.573681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8.1
Q118.25
median30
Q345
95-th percentile118.5
Maximum120
Range118
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation28.15478577
Coefficient of variation (CV)0.7586252577
Kurtosis2.978937057
Mean37.11290323
Median Absolute Deviation (MAD)15
Skewness1.75142393
Sum2301
Variance792.6919619
MonotonicityNot monotonic
2022-09-04T23:44:32.646689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4516
17.8%
308
 
8.9%
178
 
8.9%
205
 
5.6%
1204
 
4.4%
253
 
3.3%
233
 
3.3%
602
 
2.2%
902
 
2.2%
41
 
1.1%
Other values (10)10
 
11.1%
(Missing)28
31.1%
ValueCountFrequency (%)
21
 
1.1%
41
 
1.1%
51
 
1.1%
81
 
1.1%
101
 
1.1%
121
 
1.1%
151
 
1.1%
178
8.9%
181
 
1.1%
191
 
1.1%
ValueCountFrequency (%)
1204
 
4.4%
902
 
2.2%
602
 
2.2%
551
 
1.1%
4516
17.8%
331
 
1.1%
308
8.9%
253
 
3.3%
233
 
3.3%
205
 
5.6%

_embedded.show.averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct34
Distinct (%)40.0%
Missing5
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean31.47058824
Minimum2
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-09-04T23:44:32.722679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.4
Q115
median24
Q345
95-th percentile87.4
Maximum120
Range118
Interquartile range (IQR)30

Descriptive statistics

Standard deviation24.77113731
Coefficient of variation (CV)0.787120251
Kurtosis3.863232753
Mean31.47058824
Median Absolute Deviation (MAD)9
Skewness1.852148949
Sum2675
Variance613.6092437
MonotonicityNot monotonic
2022-09-04T23:44:32.805688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
4514
15.6%
1512
13.3%
178
 
8.9%
255
 
5.6%
305
 
5.6%
205
 
5.6%
123
 
3.3%
422
 
2.2%
602
 
2.2%
1202
 
2.2%
Other values (24)27
30.0%
(Missing)5
 
5.6%
ValueCountFrequency (%)
21
 
1.1%
32
2.2%
41
 
1.1%
51
 
1.1%
71
 
1.1%
91
 
1.1%
101
 
1.1%
111
 
1.1%
123
3.3%
141
 
1.1%
ValueCountFrequency (%)
1202
 
2.2%
1101
 
1.1%
981
 
1.1%
901
 
1.1%
771
 
1.1%
602
 
2.2%
591
 
1.1%
551
 
1.1%
491
 
1.1%
4514
15.6%

_embedded.show.premiered
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct54
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size848.0 B
2020-12-23
17 
2020-12-16
 
5
2020-12-21
 
4
2020-12-14
 
3
2020-11-19
 
3
Other values (49)
58 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters900
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)46.7%

Sample

1st row2019-04-03
2nd row2020-12-17
3rd row2020-07-11
4th row2020-08-12
5th row2019-10-08

Common Values

ValueCountFrequency (%)
2020-12-2317
 
18.9%
2020-12-165
 
5.6%
2020-12-214
 
4.4%
2020-12-143
 
3.3%
2020-11-193
 
3.3%
2020-12-083
 
3.3%
2020-11-183
 
3.3%
2020-12-022
 
2.2%
2017-02-102
 
2.2%
2017-01-262
 
2.2%
Other values (44)46
51.1%

Length

2022-09-04T23:44:32.884776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-2317
 
18.9%
2020-12-165
 
5.6%
2020-12-214
 
4.4%
2020-12-143
 
3.3%
2020-11-193
 
3.3%
2020-12-083
 
3.3%
2020-11-183
 
3.3%
2017-01-262
 
2.2%
2013-12-242
 
2.2%
2020-11-232
 
2.2%
Other values (44)46
51.1%

Most occurring characters

ValueCountFrequency (%)
2237
26.3%
0209
23.2%
-180
20.0%
1158
17.6%
327
 
3.0%
920
 
2.2%
819
 
2.1%
416
 
1.8%
713
 
1.4%
611
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number720
80.0%
Dash Punctuation180
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2237
32.9%
0209
29.0%
1158
21.9%
327
 
3.8%
920
 
2.8%
819
 
2.6%
416
 
2.2%
713
 
1.8%
611
 
1.5%
510
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
-180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2237
26.3%
0209
23.2%
-180
20.0%
1158
17.6%
327
 
3.0%
920
 
2.2%
819
 
2.1%
416
 
1.8%
713
 
1.4%
611
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2237
26.3%
0209
23.2%
-180
20.0%
1158
17.6%
327
 
3.0%
920
 
2.2%
819
 
2.1%
416
 
1.8%
713
 
1.4%
611
 
1.2%

_embedded.show.ended
Categorical

HIGH CORRELATION
MISSING

Distinct19
Distinct (%)38.8%
Missing41
Missing (%)45.6%
Memory size848.0 B
2020-12-23
18 
2020-12-30
2021-01-05
2020-12-28
2021-01-20
Other values (14)
17 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters490
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)22.4%

Sample

1st row2020-12-25
2nd row2021-01-20
3rd row2020-12-23
4th row2020-12-23
5th row2020-12-23

Common Values

ValueCountFrequency (%)
2020-12-2318
20.0%
2020-12-305
 
5.6%
2021-01-054
 
4.4%
2020-12-283
 
3.3%
2021-01-202
 
2.2%
2021-01-272
 
2.2%
2021-01-092
 
2.2%
2021-01-142
 
2.2%
2021-03-131
 
1.1%
2021-01-131
 
1.1%
Other values (9)9
 
10.0%
(Missing)41
45.6%

Length

2022-09-04T23:44:32.953776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-2318
36.7%
2020-12-305
 
10.2%
2021-01-054
 
8.2%
2020-12-283
 
6.1%
2021-01-202
 
4.1%
2021-01-272
 
4.1%
2021-01-092
 
4.1%
2021-01-142
 
4.1%
2022-01-141
 
2.0%
2021-01-061
 
2.0%
Other values (9)9
18.4%

Most occurring characters

ValueCountFrequency (%)
2158
32.2%
0114
23.3%
-98
20.0%
170
14.3%
330
 
6.1%
55
 
1.0%
84
 
0.8%
44
 
0.8%
63
 
0.6%
72
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number392
80.0%
Dash Punctuation98
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2158
40.3%
0114
29.1%
170
17.9%
330
 
7.7%
55
 
1.3%
84
 
1.0%
44
 
1.0%
63
 
0.8%
72
 
0.5%
92
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
-98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common490
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2158
32.2%
0114
23.3%
-98
20.0%
170
14.3%
330
 
6.1%
55
 
1.0%
84
 
0.8%
44
 
0.8%
63
 
0.6%
72
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2158
32.2%
0114
23.3%
-98
20.0%
170
14.3%
330
 
6.1%
55
 
1.0%
84
 
0.8%
44
 
0.8%
63
 
0.6%
72
 
0.4%

_embedded.show.officialSite
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct55
Distinct (%)78.6%
Missing20
Missing (%)22.2%
Memory size848.0 B
https://www.wavve.com/player/vod?programid=C9901_C99000000049
https://www.tytnetwork.com
 
2
https://www.joyn.de/serien/jerks
 
2
https://v.qq.com/x/search/?q=+%E4%BB%8A%E5%A4%95%E4%BD%95%E5%A4%95&stag=0&smartbox_ab=
 
2
https://v.qq.com/detail/m/mzc00200dnvb1wh.html
 
2
Other values (50)
54 

Length

Max length97
Median length62
Mean length51.52857143
Min length18

Characters and Unicode

Total characters3607
Distinct characters74
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)65.7%

Sample

1st rowhttps://www.youtube.com/playlist?list=PLZ1FUdedsrSJubWkFFh5vKHzawzQk1mYI
2nd rowhttps://www.ivi.ru/watch/muzhskaya-tema
3rd rowhttps://v.qq.com/detail/m/mzc00200hc38s5x.html
4th rowhttps://v.qq.com/detail/w/ww18u675tfmhas6.html
5th rowhttps://www.wavve.com/player/vod?programid=C9901_C99000000049

Common Values

ValueCountFrequency (%)
https://www.wavve.com/player/vod?programid=C9901_C990000000498
 
8.9%
https://www.tytnetwork.com2
 
2.2%
https://www.joyn.de/serien/jerks2
 
2.2%
https://v.qq.com/x/search/?q=+%E4%BB%8A%E5%A4%95%E4%BD%95%E5%A4%95&stag=0&smartbox_ab=2
 
2.2%
https://v.qq.com/detail/m/mzc00200dnvb1wh.html2
 
2.2%
https://www.iqiyi.com/a_19rrhllpip.html2
 
2.2%
https://v.qq.com/detail/m/mzc00200ur8p8zp.html2
 
2.2%
https://so.youku.com/search_video/q_%20%E6%9C%80%E5%88%9D%E7%9A%84%E7%9B%B8%E9%81%87?searchfrom=12
 
2.2%
https://tv.nrk.no/serie/verdens-minste-kommentatorboks2
 
2.2%
http://gtst.nl/#!/1
 
1.1%
Other values (45)45
50.0%
(Missing)20
22.2%

Length

2022-09-04T23:44:33.040169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.wavve.com/player/vod?programid=c9901_c990000000498
 
11.4%
https://www.joyn.de/serien/jerks2
 
2.9%
https://v.qq.com/x/search/?q=+%e4%bb%8a%e5%a4%95%e4%bd%95%e5%a4%95&stag=0&smartbox_ab2
 
2.9%
https://v.qq.com/detail/m/mzc00200dnvb1wh.html2
 
2.9%
https://www.iqiyi.com/a_19rrhllpip.html2
 
2.9%
https://v.qq.com/detail/m/mzc00200ur8p8zp.html2
 
2.9%
https://so.youku.com/search_video/q_%20%e6%9c%80%e5%88%9d%e7%9a%84%e7%9b%b8%e9%81%87?searchfrom=12
 
2.9%
https://tv.nrk.no/serie/verdens-minste-kommentatorboks2
 
2.9%
https://www.tytnetwork.com2
 
2.9%
http://www.njpw1972.com1
 
1.4%
Other values (45)45
64.3%

Most occurring characters

ValueCountFrequency (%)
/280
 
7.8%
t253
 
7.0%
s166
 
4.6%
w163
 
4.5%
o162
 
4.5%
e144
 
4.0%
.141
 
3.9%
h131
 
3.6%
p127
 
3.5%
m122
 
3.4%
Other values (64)1918
53.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2295
63.6%
Other Punctuation579
 
16.1%
Decimal Number383
 
10.6%
Uppercase Letter271
 
7.5%
Math Symbol31
 
0.9%
Dash Punctuation29
 
0.8%
Connector Punctuation19
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t253
 
11.0%
s166
 
7.2%
w163
 
7.1%
o162
 
7.1%
e144
 
6.3%
h131
 
5.7%
p127
 
5.5%
m122
 
5.3%
a113
 
4.9%
r104
 
4.5%
Other values (16)810
35.3%
Uppercase Letter
ValueCountFrequency (%)
C27
 
10.0%
E24
 
8.9%
B21
 
7.7%
P15
 
5.5%
L15
 
5.5%
A13
 
4.8%
Y12
 
4.4%
H12
 
4.4%
Q11
 
4.1%
S11
 
4.1%
Other values (16)110
40.6%
Decimal Number
ValueCountFrequency (%)
0104
27.2%
974
19.3%
142
11.0%
834
 
8.9%
531
 
8.1%
426
 
6.8%
621
 
5.5%
719
 
5.0%
219
 
5.0%
313
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/280
48.4%
.141
24.4%
:70
 
12.1%
%57
 
9.8%
?22
 
3.8%
&7
 
1.2%
!1
 
0.2%
#1
 
0.2%
Math Symbol
ValueCountFrequency (%)
=29
93.5%
+2
 
6.5%
Dash Punctuation
ValueCountFrequency (%)
-29
100.0%
Connector Punctuation
ValueCountFrequency (%)
_19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2566
71.1%
Common1041
28.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t253
 
9.9%
s166
 
6.5%
w163
 
6.4%
o162
 
6.3%
e144
 
5.6%
h131
 
5.1%
p127
 
4.9%
m122
 
4.8%
a113
 
4.4%
r104
 
4.1%
Other values (42)1081
42.1%
Common
ValueCountFrequency (%)
/280
26.9%
.141
13.5%
0104
 
10.0%
974
 
7.1%
:70
 
6.7%
%57
 
5.5%
142
 
4.0%
834
 
3.3%
531
 
3.0%
=29
 
2.8%
Other values (12)179
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/280
 
7.8%
t253
 
7.0%
s166
 
4.6%
w163
 
4.5%
o162
 
4.5%
e144
 
4.0%
.141
 
3.9%
h131
 
3.6%
p127
 
3.5%
m122
 
3.4%
Other values (64)1918
53.2%

_embedded.show.schedule.time
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size848.0 B
64 
20:00
13 
19:00
 
3
10:00
 
2
00:00
 
2
Other values (6)
 
6

Length

Max length5
Median length0
Mean length1.444444444
Min length0

Characters and Unicode

Total characters130
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)6.7%

Sample

1st row
2nd row12:00
3rd row10:00
4th row10:00
5th row

Common Values

ValueCountFrequency (%)
64
71.1%
20:0013
 
14.4%
19:003
 
3.3%
10:002
 
2.2%
00:002
 
2.2%
12:001
 
1.1%
06:001
 
1.1%
20:451
 
1.1%
20:301
 
1.1%
22:001
 
1.1%

Length

2022-09-04T23:44:33.129170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20:0013
50.0%
19:003
 
11.5%
10:002
 
7.7%
00:002
 
7.7%
12:001
 
3.8%
06:001
 
3.8%
20:451
 
3.8%
20:301
 
3.8%
22:001
 
3.8%
21:301
 
3.8%

Most occurring characters

ValueCountFrequency (%)
070
53.8%
:26
 
20.0%
219
 
14.6%
17
 
5.4%
93
 
2.3%
32
 
1.5%
61
 
0.8%
41
 
0.8%
51
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number104
80.0%
Other Punctuation26
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
070
67.3%
219
 
18.3%
17
 
6.7%
93
 
2.9%
32
 
1.9%
61
 
1.0%
41
 
1.0%
51
 
1.0%
Other Punctuation
ValueCountFrequency (%)
:26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
070
53.8%
:26
 
20.0%
219
 
14.6%
17
 
5.4%
93
 
2.3%
32
 
1.5%
61
 
0.8%
41
 
0.8%
51
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
070
53.8%
:26
 
20.0%
219
 
14.6%
17
 
5.4%
93
 
2.3%
32
 
1.5%
61
 
0.8%
41
 
0.8%
51
 
0.8%

_embedded.show.schedule.days
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size848.0 B

_embedded.show.rating.average
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)85.7%
Missing83
Missing (%)92.2%
Infinite0
Infinite (%)0.0%
Mean6.471428571
Minimum3.6
Maximum8.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-09-04T23:44:33.194170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile3.72
Q15.45
median7.2
Q37.4
95-th percentile8.44
Maximum8.8
Range5.2
Interquartile range (IQR)1.95

Descriptive statistics

Standard deviation1.927618812
Coefficient of variation (CV)0.2978660416
Kurtosis-0.7724134434
Mean6.471428571
Median Absolute Deviation (MAD)0.4
Skewness-0.7771249997
Sum45.3
Variance3.715714286
MonotonicityNot monotonic
2022-09-04T23:44:33.263170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
7.22
 
2.2%
8.81
 
1.1%
3.61
 
1.1%
7.61
 
1.1%
6.91
 
1.1%
41
 
1.1%
(Missing)83
92.2%
ValueCountFrequency (%)
3.61
1.1%
41
1.1%
6.91
1.1%
7.22
2.2%
7.61
1.1%
8.81
1.1%
ValueCountFrequency (%)
8.81
1.1%
7.61
1.1%
7.22
2.2%
6.91
1.1%
41
1.1%
3.61
1.1%

_embedded.show.weight
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.12222222
Minimum3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-09-04T23:44:33.344168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q118
median30
Q352
95-th percentile86.2
Maximum100
Range97
Interquartile range (IQR)34

Descriptive statistics

Standard deviation24.4259154
Coefficient of variation (CV)0.6954547252
Kurtosis0.2421407248
Mean35.12222222
Median Absolute Deviation (MAD)15.5
Skewness0.9560976307
Sum3161
Variance596.6253433
MonotonicityNot monotonic
2022-09-04T23:44:33.435167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3210
 
11.1%
529
 
10.0%
86
 
6.7%
45
 
5.6%
204
 
4.4%
244
 
4.4%
443
 
3.3%
223
 
3.3%
233
 
3.3%
303
 
3.3%
Other values (30)40
44.4%
ValueCountFrequency (%)
31
 
1.1%
45
5.6%
86
6.7%
101
 
1.1%
122
 
2.2%
131
 
1.1%
142
 
2.2%
152
 
2.2%
161
 
1.1%
171
 
1.1%
ValueCountFrequency (%)
1001
1.1%
981
1.1%
931
1.1%
901
1.1%
881
1.1%
841
1.1%
802
2.2%
792
2.2%
741
1.1%
661
1.1%

_embedded.show.network
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing90
Missing (%)100.0%
Memory size848.0 B

_embedded.show.webChannel.id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)34.8%
Missing1
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean128.0337079
Minimum3
Maximum516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-09-04T23:44:33.638828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile21
Q121
median104
Q3173
95-th percentile379.6
Maximum516
Range513
Interquartile range (IQR)152

Descriptive statistics

Standard deviation122.4099344
Coefficient of variation (CV)0.9560758367
Kurtosis1.085945013
Mean128.0337079
Median Absolute Deviation (MAD)83
Skewness1.339805841
Sum11395
Variance14984.19203
MonotonicityNot monotonic
2022-09-04T23:44:33.714828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2121
23.3%
11416
17.8%
10410
11.1%
304
 
4.4%
263
 
3.3%
1183
 
3.3%
2383
 
3.3%
2262
 
2.2%
3792
 
2.2%
3562
 
2.2%
Other values (21)23
25.6%
ValueCountFrequency (%)
31
 
1.1%
151
 
1.1%
201
 
1.1%
2121
23.3%
263
 
3.3%
304
 
4.4%
451
 
1.1%
511
 
1.1%
672
 
2.2%
881
 
1.1%
ValueCountFrequency (%)
5161
1.1%
4521
1.1%
4451
1.1%
4141
1.1%
3801
1.1%
3792
2.2%
3562
2.2%
3371
1.1%
3271
1.1%
3111
1.1%

_embedded.show.webChannel.name
Categorical

HIGH CORRELATION
MISSING

Distinct31
Distinct (%)34.8%
Missing1
Missing (%)1.1%
Memory size848.0 B
YouTube
21 
wavve
16 
Tencent QQ
10 
Naver TVCast
BBC iPlayer
 
3
Other values (26)
35 

Length

Max length17
Median length14
Mean length7.528089888
Min length3

Characters and Unicode

Total characters670
Distinct characters48
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)21.3%

Sample

1st rowYouTube
2nd rowivi
3rd rowTencent QQ
4th rowTencent QQ
5th rowSeezn

Common Values

ValueCountFrequency (%)
YouTube21
23.3%
wavve16
17.8%
Tencent QQ10
11.1%
Naver TVCast4
 
4.4%
BBC iPlayer3
 
3.3%
Youku3
 
3.3%
NRK TV3
 
3.3%
Mango TV2
 
2.2%
Shahid2
 
2.2%
Joyn2
 
2.2%
Other values (21)23
25.6%

Length

2022-09-04T23:44:33.793833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
youtube21
17.1%
wavve16
 
13.0%
tencent10
 
8.1%
qq10
 
8.1%
tv8
 
6.5%
naver4
 
3.3%
tvcast4
 
3.3%
bbc3
 
2.4%
iplayer3
 
2.4%
youku3
 
2.4%
Other values (34)41
33.3%

Most occurring characters

ValueCountFrequency (%)
e77
 
11.5%
u53
 
7.9%
T45
 
6.7%
v38
 
5.7%
o38
 
5.7%
a37
 
5.5%
34
 
5.1%
n27
 
4.0%
Y26
 
3.9%
b24
 
3.6%
Other values (38)271
40.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter440
65.7%
Uppercase Letter193
28.8%
Space Separator34
 
5.1%
Math Symbol1
 
0.1%
Decimal Number1
 
0.1%
Other Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e77
17.5%
u53
12.0%
v38
8.6%
o38
8.6%
a37
8.4%
n27
 
6.1%
b24
 
5.5%
t21
 
4.8%
i19
 
4.3%
w17
 
3.9%
Other values (12)89
20.2%
Uppercase Letter
ValueCountFrequency (%)
T45
23.3%
Y26
13.5%
Q22
11.4%
V14
 
7.3%
N10
 
5.2%
B10
 
5.2%
C10
 
5.2%
P9
 
4.7%
W7
 
3.6%
L7
 
3.6%
Other values (12)33
17.1%
Space Separator
ValueCountFrequency (%)
34
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%
Decimal Number
ValueCountFrequency (%)
21
100.0%
Other Punctuation
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin633
94.5%
Common37
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e77
 
12.2%
u53
 
8.4%
T45
 
7.1%
v38
 
6.0%
o38
 
6.0%
a37
 
5.8%
n27
 
4.3%
Y26
 
4.1%
b24
 
3.8%
Q22
 
3.5%
Other values (34)246
38.9%
Common
ValueCountFrequency (%)
34
91.9%
+1
 
2.7%
21
 
2.7%
.1
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e77
 
11.5%
u53
 
7.9%
T45
 
6.7%
v38
 
5.7%
o38
 
5.7%
a37
 
5.5%
34
 
5.1%
n27
 
4.0%
Y26
 
3.9%
b24
 
3.6%
Other values (38)271
40.4%

_embedded.show.webChannel.country
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing90
Missing (%)100.0%
Memory size848.0 B

_embedded.show.webChannel.officialSite
Categorical

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)20.3%
Missing26
Missing (%)28.9%
Memory size848.0 B
https://www.youtube.com
21 
https://www.wavve.com/
16 
https://v.qq.com/
10 
https://tv.naver.com/
https://www.bbc.co.uk/iplayer
Other values (8)
10 

Length

Max length30
Median length29
Mean length21.921875
Min length17

Characters and Unicode

Total characters1403
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)9.4%

Sample

1st rowhttps://www.youtube.com
2nd rowhttps://www.ivi.ru/
3rd rowhttps://v.qq.com/
4th rowhttps://v.qq.com/
5th rowhttps://www.seezntv.com/

Common Values

ValueCountFrequency (%)
https://www.youtube.com21
23.3%
https://www.wavve.com/16
17.8%
https://v.qq.com/10
 
11.1%
https://tv.naver.com/4
 
4.4%
https://www.bbc.co.uk/iplayer3
 
3.3%
https://w.mgtv.com/2
 
2.2%
https://www.iq.com/2
 
2.2%
https://www.ivi.ru/1
 
1.1%
https://www.seezntv.com/1
 
1.1%
https://www.primevideo.com1
 
1.1%
Other values (3)3
 
3.3%
(Missing)26
28.9%

Length

2022-09-04T23:44:33.900242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.youtube.com21
32.8%
https://www.wavve.com16
25.0%
https://v.qq.com10
15.6%
https://tv.naver.com4
 
6.2%
https://www.bbc.co.uk/iplayer3
 
4.7%
https://w.mgtv.com2
 
3.1%
https://www.iq.com2
 
3.1%
https://www.ivi.ru1
 
1.6%
https://www.seezntv.com1
 
1.6%
https://www.primevideo.com1
 
1.6%
Other values (3)3
 
4.7%

Most occurring characters

ValueCountFrequency (%)
/169
12.0%
w165
11.8%
t159
11.3%
.131
 
9.3%
o86
 
6.1%
p71
 
5.1%
s69
 
4.9%
c66
 
4.7%
h64
 
4.6%
:64
 
4.6%
Other values (16)359
25.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1039
74.1%
Other Punctuation364
 
25.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w165
15.9%
t159
15.3%
o86
8.3%
p71
 
6.8%
s69
 
6.6%
c66
 
6.4%
h64
 
6.2%
m62
 
6.0%
v57
 
5.5%
e52
 
5.0%
Other values (13)188
18.1%
Other Punctuation
ValueCountFrequency (%)
/169
46.4%
.131
36.0%
:64
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
Latin1039
74.1%
Common364
 
25.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
w165
15.9%
t159
15.3%
o86
8.3%
p71
 
6.8%
s69
 
6.6%
c66
 
6.4%
h64
 
6.2%
m62
 
6.0%
v57
 
5.5%
e52
 
5.0%
Other values (13)188
18.1%
Common
ValueCountFrequency (%)
/169
46.4%
.131
36.0%
:64
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/169
12.0%
w165
11.8%
t159
11.3%
.131
 
9.3%
o86
 
6.1%
p71
 
5.1%
s69
 
4.9%
c66
 
4.7%
h64
 
4.6%
:64
 
4.6%
Other values (16)359
25.6%

_embedded.show.dvdCountry
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing90
Missing (%)100.0%
Memory size848.0 B

_embedded.show.externals.tvrage
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing87
Missing (%)96.7%
Memory size848.0 B
41967.0
19056.0
25100.0

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters21
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row41967.0
2nd row19056.0
3rd row25100.0

Common Values

ValueCountFrequency (%)
41967.01
 
1.1%
19056.01
 
1.1%
25100.01
 
1.1%
(Missing)87
96.7%

Length

2022-09-04T23:44:33.979373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:34.053373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
41967.01
33.3%
19056.01
33.3%
25100.01
33.3%

Most occurring characters

ValueCountFrequency (%)
06
28.6%
13
14.3%
.3
14.3%
92
 
9.5%
62
 
9.5%
52
 
9.5%
41
 
4.8%
71
 
4.8%
21
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18
85.7%
Other Punctuation3
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06
33.3%
13
16.7%
92
 
11.1%
62
 
11.1%
52
 
11.1%
41
 
5.6%
71
 
5.6%
21
 
5.6%
Other Punctuation
ValueCountFrequency (%)
.3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06
28.6%
13
14.3%
.3
14.3%
92
 
9.5%
62
 
9.5%
52
 
9.5%
41
 
4.8%
71
 
4.8%
21
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06
28.6%
13
14.3%
.3
14.3%
92
 
9.5%
62
 
9.5%
52
 
9.5%
41
 
4.8%
71
 
4.8%
21
 
4.8%

_embedded.show.externals.thetvdb
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct45
Distinct (%)75.0%
Missing30
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean362741.1833
Minimum104271
Maximum410187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-09-04T23:44:34.131373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum104271
5-th percentile278113
Q1355994.25
median389347
Q3393547
95-th percentile400153.75
Maximum410187
Range305916
Interquartile range (IQR)37552.75

Descriptive statistics

Standard deviation58139.2392
Coefficient of variation (CV)0.1602774702
Kurtosis8.432234987
Mean362741.1833
Median Absolute Deviation (MAD)10392
Skewness-2.661264166
Sum21764471
Variance3380171135
MonotonicityNot monotonic
2022-09-04T23:44:34.224373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
3997398
 
8.9%
3924102
 
2.2%
3922142
 
2.2%
2787932
 
2.2%
3933812
 
2.2%
3893472
 
2.2%
3231442
 
2.2%
3926792
 
2.2%
4101872
 
2.2%
3933371
 
1.1%
Other values (35)35
38.9%
(Missing)30
33.3%
ValueCountFrequency (%)
1042711
1.1%
1445411
1.1%
2651931
1.1%
2787932
2.2%
2806191
1.1%
2906861
1.1%
3150611
1.1%
3174761
1.1%
3213641
1.1%
3231442
2.2%
ValueCountFrequency (%)
4101872
 
2.2%
4080341
 
1.1%
3997398
8.9%
3952351
 
1.1%
3946271
 
1.1%
3940871
 
1.1%
3940451
 
1.1%
3933812
 
2.2%
3933371
 
1.1%
3927521
 
1.1%

_embedded.show.externals.imdb
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct30
Distinct (%)78.9%
Missing52
Missing (%)57.8%
Memory size848.0 B
tt11815724
 
2
tt13598988
 
2
tt13568876
 
2
tt1714810
 
2
tt6071060
 
2
Other values (25)
28 

Length

Max length10
Median length10
Mean length9.552631579
Min length9

Characters and Unicode

Total characters363
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)57.9%

Sample

1st rowtt13695606
2nd rowtt13423446
3rd rowtt14125832
4th rowtt14125832
5th rowtt13470370

Common Values

ValueCountFrequency (%)
tt118157242
 
2.2%
tt135989882
 
2.2%
tt135688762
 
2.2%
tt17148102
 
2.2%
tt60710602
 
2.2%
tt135990002
 
2.2%
tt134703702
 
2.2%
tt141258322
 
2.2%
tt85943241
 
1.1%
tt60063501
 
1.1%
Other values (20)20
 
22.2%
(Missing)52
57.8%

Length

2022-09-04T23:44:34.322557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tt118157242
 
5.3%
tt135688762
 
5.3%
tt17148102
 
5.3%
tt60710602
 
5.3%
tt135990002
 
5.3%
tt134703702
 
5.3%
tt141258322
 
5.3%
tt135989882
 
5.3%
tt91695981
 
2.6%
tt129009661
 
2.6%
Other values (20)20
52.6%

Most occurring characters

ValueCountFrequency (%)
t76
20.9%
145
12.4%
038
10.5%
632
8.8%
831
8.5%
430
 
8.3%
327
 
7.4%
923
 
6.3%
222
 
6.1%
521
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number287
79.1%
Lowercase Letter76
 
20.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
145
15.7%
038
13.2%
632
11.1%
831
10.8%
430
10.5%
327
9.4%
923
8.0%
222
7.7%
521
7.3%
718
 
6.3%
Lowercase Letter
ValueCountFrequency (%)
t76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common287
79.1%
Latin76
 
20.9%

Most frequent character per script

Common
ValueCountFrequency (%)
145
15.7%
038
13.2%
632
11.1%
831
10.8%
430
10.5%
327
9.4%
923
8.0%
222
7.7%
521
7.3%
718
 
6.3%
Latin
ValueCountFrequency (%)
t76
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t76
20.9%
145
12.4%
038
10.5%
632
8.8%
831
8.5%
430
 
8.3%
327
 
7.4%
923
 
6.3%
222
 
6.1%
521
 
5.8%

_embedded.show.image.medium
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct63
Distinct (%)71.6%
Missing2
Missing (%)2.2%
Memory size848.0 B
https://static.tvmaze.com/uploads/images/medium_portrait/294/735240.jpg
https://static.tvmaze.com/uploads/images/medium_portrait/306/765134.jpg
https://static.tvmaze.com/uploads/images/medium_portrait/395/987553.jpg
 
2
https://static.tvmaze.com/uploads/images/medium_portrait/370/926884.jpg
 
2
https://static.tvmaze.com/uploads/images/medium_portrait/288/721432.jpg
 
2
Other values (58)
66 

Length

Max length72
Median length71
Mean length71.04545455
Min length70

Characters and Unicode

Total characters6252
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)56.8%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_portrait/315/789854.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_portrait/289/723328.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_portrait/270/675333.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_portrait/267/669816.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_portrait/290/727378.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_portrait/294/735240.jpg8
 
8.9%
https://static.tvmaze.com/uploads/images/medium_portrait/306/765134.jpg8
 
8.9%
https://static.tvmaze.com/uploads/images/medium_portrait/395/987553.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/medium_portrait/370/926884.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/medium_portrait/288/721432.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/medium_portrait/288/721821.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/medium_portrait/289/723488.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/medium_portrait/291/729147.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/medium_portrait/51/129595.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/medium_portrait/285/713040.jpg2
 
2.2%
Other values (53)56
62.2%

Length

2022-09-04T23:44:34.413557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_portrait/294/735240.jpg8
 
9.1%
https://static.tvmaze.com/uploads/images/medium_portrait/306/765134.jpg8
 
9.1%
https://static.tvmaze.com/uploads/images/medium_portrait/291/729147.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/medium_portrait/285/714863.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/medium_portrait/296/740333.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/medium_portrait/285/713040.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/medium_portrait/51/129595.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/medium_portrait/414/1035476.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/medium_portrait/289/723488.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/medium_portrait/288/721821.jpg2
 
2.3%
Other values (53)56
63.6%

Most occurring characters

ValueCountFrequency (%)
t616
 
9.9%
/616
 
9.9%
m440
 
7.0%
a440
 
7.0%
p352
 
5.6%
s352
 
5.6%
i352
 
5.6%
o264
 
4.2%
.264
 
4.2%
e264
 
4.2%
Other values (22)2292
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4400
70.4%
Other Punctuation968
 
15.5%
Decimal Number796
 
12.7%
Connector Punctuation88
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t616
14.0%
m440
10.0%
a440
10.0%
p352
 
8.0%
s352
 
8.0%
i352
 
8.0%
o264
 
6.0%
e264
 
6.0%
u176
 
4.0%
c176
 
4.0%
Other values (8)968
22.0%
Decimal Number
ValueCountFrequency (%)
2105
13.2%
795
11.9%
394
11.8%
482
10.3%
877
9.7%
974
9.3%
171
8.9%
067
8.4%
667
8.4%
564
8.0%
Other Punctuation
ValueCountFrequency (%)
/616
63.6%
.264
27.3%
:88
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4400
70.4%
Common1852
29.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t616
14.0%
m440
10.0%
a440
10.0%
p352
 
8.0%
s352
 
8.0%
i352
 
8.0%
o264
 
6.0%
e264
 
6.0%
u176
 
4.0%
c176
 
4.0%
Other values (8)968
22.0%
Common
ValueCountFrequency (%)
/616
33.3%
.264
14.3%
2105
 
5.7%
795
 
5.1%
394
 
5.1%
_88
 
4.8%
:88
 
4.8%
482
 
4.4%
877
 
4.2%
974
 
4.0%
Other values (4)269
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII6252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t616
 
9.9%
/616
 
9.9%
m440
 
7.0%
a440
 
7.0%
p352
 
5.6%
s352
 
5.6%
i352
 
5.6%
o264
 
4.2%
.264
 
4.2%
e264
 
4.2%
Other values (22)2292
36.7%

_embedded.show.image.original
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct63
Distinct (%)71.6%
Missing2
Missing (%)2.2%
Memory size848.0 B
https://static.tvmaze.com/uploads/images/original_untouched/294/735240.jpg
https://static.tvmaze.com/uploads/images/original_untouched/306/765134.jpg
https://static.tvmaze.com/uploads/images/original_untouched/395/987553.jpg
 
2
https://static.tvmaze.com/uploads/images/original_untouched/370/926884.jpg
 
2
https://static.tvmaze.com/uploads/images/original_untouched/288/721432.jpg
 
2
Other values (58)
66 

Length

Max length75
Median length74
Mean length74.04545455
Min length73

Characters and Unicode

Total characters6516
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)56.8%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/315/789854.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/289/723328.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/270/675333.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/267/669816.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/290/727378.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/294/735240.jpg8
 
8.9%
https://static.tvmaze.com/uploads/images/original_untouched/306/765134.jpg8
 
8.9%
https://static.tvmaze.com/uploads/images/original_untouched/395/987553.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/original_untouched/370/926884.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/original_untouched/288/721432.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/original_untouched/288/721821.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/original_untouched/289/723488.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/original_untouched/291/729147.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/original_untouched/51/129595.jpg2
 
2.2%
https://static.tvmaze.com/uploads/images/original_untouched/285/713040.jpg2
 
2.2%
Other values (53)56
62.2%

Length

2022-09-04T23:44:34.504556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/294/735240.jpg8
 
9.1%
https://static.tvmaze.com/uploads/images/original_untouched/306/765134.jpg8
 
9.1%
https://static.tvmaze.com/uploads/images/original_untouched/291/729147.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/original_untouched/285/714863.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/original_untouched/296/740333.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/original_untouched/285/713040.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/original_untouched/51/129595.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/original_untouched/414/1035476.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/original_untouched/289/723488.jpg2
 
2.3%
https://static.tvmaze.com/uploads/images/original_untouched/288/721821.jpg2
 
2.3%
Other values (53)56
63.6%

Most occurring characters

ValueCountFrequency (%)
/616
 
9.5%
t528
 
8.1%
a440
 
6.8%
s352
 
5.4%
i352
 
5.4%
o352
 
5.4%
p264
 
4.1%
c264
 
4.1%
.264
 
4.1%
g264
 
4.1%
Other values (23)2820
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4664
71.6%
Other Punctuation968
 
14.9%
Decimal Number796
 
12.2%
Connector Punctuation88
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t528
 
11.3%
a440
 
9.4%
s352
 
7.5%
i352
 
7.5%
o352
 
7.5%
p264
 
5.7%
c264
 
5.7%
g264
 
5.7%
m264
 
5.7%
e264
 
5.7%
Other values (9)1320
28.3%
Decimal Number
ValueCountFrequency (%)
2105
13.2%
795
11.9%
394
11.8%
482
10.3%
877
9.7%
974
9.3%
171
8.9%
067
8.4%
667
8.4%
564
8.0%
Other Punctuation
ValueCountFrequency (%)
/616
63.6%
.264
27.3%
:88
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4664
71.6%
Common1852
 
28.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t528
 
11.3%
a440
 
9.4%
s352
 
7.5%
i352
 
7.5%
o352
 
7.5%
p264
 
5.7%
c264
 
5.7%
g264
 
5.7%
m264
 
5.7%
e264
 
5.7%
Other values (9)1320
28.3%
Common
ValueCountFrequency (%)
/616
33.3%
.264
14.3%
2105
 
5.7%
795
 
5.1%
394
 
5.1%
:88
 
4.8%
_88
 
4.8%
482
 
4.4%
877
 
4.2%
974
 
4.0%
Other values (4)269
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII6516
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/616
 
9.5%
t528
 
8.1%
a440
 
6.8%
s352
 
5.4%
i352
 
5.4%
o352
 
5.4%
p264
 
4.1%
c264
 
4.1%
.264
 
4.1%
g264
 
4.1%
Other values (23)2820
43.3%

_embedded.show.summary
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct56
Distinct (%)69.1%
Missing9
Missing (%)10.0%
Memory size848.0 B
<p><b>Half-Fifty</b> is a comedy drama about youth and growth, and the series follows a group of 25-year-olds who end up in the world of YouTubers.</p>
<p><b>100% Era</b> imagines the post-corona future. How the lives of kids born during the pandemic will look like? What will humanity and teens look like in 2044? Our teenagers living in a more competitive world, where the world no longer needs human hands. For humans, the competition to rise to the top is intensifying. How to secretly love children in the era when schools disappeared and contactless. Hee Jae and Shi Dae attend the same top rank education academy. It focuses on training kids to score 100% on tests, while kids who score below 90% get expelled. In such an environment, Shi Dae and Hee Jae spend their teenage years and grow together.</p>
<p>A story that follows a detective in the major crimes division of Nan Xing City Police Department. Together with a woman who has super memory, he upholds the law one case at a time in solving murders, burglaries and bringing down a narcotics manufacturing facility. Jing Chu is a young and capable detective. Due to his repeated merits from cracking big cases, he is promoted to the position of major crimes division vice-captain at Nan Xing city and starts to work alongside his new team. Because of a murder case, he meets Yang Mian Mian, a young woman who possesses a photographic memory. He soon realizes that Mian Mian seems to have a deep connection to his father's mysterious death many years ago. Meanwhile, a famous blogger and a member of an idol girl group die</p>
 
2
<p>During the Yin Dynasty, Dong Yue, a brave general in the Dingyuan Rebellion, was sent back in time to stop a war that would claim the lives of countless innocents. She sets out to murder corrupted officer Lu Yuantong in an attempt to prevent war, and during her journey she met Feng Xi and Pang Yu. Pang Yu and Feng Xi were old friends who cared deeply for each other, but fell out and turn into enemies. While trying to reconcile the two brothers, Dong Yue also tries to stop Lu Yuantang's evil schemes which are poised to tear the nation apart with their help.</p>
 
2
<p>Two unlikely individuals join forces to find the truth behind a series of murders using an unconventional method. Chen Si, a female detective with a sense of justice, unexpectedly becomess partners with Yuan Shuai, a dream interpreter with a dark past.</p>
 
2
Other values (51)
59 

Length

Max length807
Median length457
Mean length343.037037
Min length53

Characters and Unicode

Total characters27786
Distinct characters97
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)53.1%

Sample

1st row<p><b>Мужская тема</b> is a symbiosis of talk shows and modern podcasts, where male celebrities answer questions that concern people in the XXI century. Bright representatives of show business, theater, pop, cinema, sports, as well as Internet stars meet in the barbershop. Here, on male territory, they can openly discuss a variety of topics, sometimes seriously, and sometimes with humor. This is a chance to see the idol in a confidential communication without notes, compare his opinion with your own and hear what men really talk about when there is not a single girl around.</p>
2nd row<p>In ancient Shenzhou, humans and demons had been in constant dispute for thousands of years. The demon princess from Tushan, Bai Binglan, and the human Zhang Kuangyun met each other due to a misunderstanding. In order to investigate the enemy country, Bai Binglan became Zhang Kuangyun's companion. As they travel together, Zhang Kuangyun discovers a conspiracy...</p>
3rd row<p>One will to create oceans. One will to summon the mulberry fields.<br /><br />One will to slaughter countless devils. One will to eradicate innumerable immortals.<br /><br />Only my will… is eternal.<br /><br />A Will Eternal tells the tale of Bai Xiaochun, an endearing but exasperating young man who is driven primarily by his fear of death and desire to live forever, but who deeply values friendship and family.<br /><br />(Source: Novel Updates)</p>
4th row<p>Da Eun works part-time and Kim Byul is an idol in her 5th years since debut. These two girls who look alike decide to change each other's lives just for 7 days. It tells the romantic encounters of these 2 girls.</p>
5th row<p><b>Half-Fifty</b> is a comedy drama about youth and growth, and the series follows a group of 25-year-olds who end up in the world of YouTubers.</p>

Common Values

ValueCountFrequency (%)
<p><b>Half-Fifty</b> is a comedy drama about youth and growth, and the series follows a group of 25-year-olds who end up in the world of YouTubers.</p>8
 
8.9%
<p><b>100% Era</b> imagines the post-corona future. How the lives of kids born during the pandemic will look like? What will humanity and teens look like in 2044? Our teenagers living in a more competitive world, where the world no longer needs human hands. For humans, the competition to rise to the top is intensifying. How to secretly love children in the era when schools disappeared and contactless. Hee Jae and Shi Dae attend the same top rank education academy. It focuses on training kids to score 100% on tests, while kids who score below 90% get expelled. In such an environment, Shi Dae and Hee Jae spend their teenage years and grow together.</p>8
 
8.9%
<p>A story that follows a detective in the major crimes division of Nan Xing City Police Department. Together with a woman who has super memory, he upholds the law one case at a time in solving murders, burglaries and bringing down a narcotics manufacturing facility. Jing Chu is a young and capable detective. Due to his repeated merits from cracking big cases, he is promoted to the position of major crimes division vice-captain at Nan Xing city and starts to work alongside his new team. Because of a murder case, he meets Yang Mian Mian, a young woman who possesses a photographic memory. He soon realizes that Mian Mian seems to have a deep connection to his father's mysterious death many years ago. Meanwhile, a famous blogger and a member of an idol girl group die</p>2
 
2.2%
<p>During the Yin Dynasty, Dong Yue, a brave general in the Dingyuan Rebellion, was sent back in time to stop a war that would claim the lives of countless innocents. She sets out to murder corrupted officer Lu Yuantong in an attempt to prevent war, and during her journey she met Feng Xi and Pang Yu. Pang Yu and Feng Xi were old friends who cared deeply for each other, but fell out and turn into enemies. While trying to reconcile the two brothers, Dong Yue also tries to stop Lu Yuantang's evil schemes which are poised to tear the nation apart with their help.</p>2
 
2.2%
<p>Two unlikely individuals join forces to find the truth behind a series of murders using an unconventional method. Chen Si, a female detective with a sense of justice, unexpectedly becomess partners with Yuan Shuai, a dream interpreter with a dark past.</p>2
 
2.2%
<p>‎At the end of the 20th century, due to the sudden decision of Xin Shensheng, gao Shan's business went bankrupt. Gao Shan wants to prove his father's innocence, but on his way suddenly falls in love with the daughter of Xin Shensheng, Tsin Waugh. Learning about the intentions of Gao Shan, Xin Shansheng makes him quit his job. ‎<br /><br />‎Gao Shan decides to go to Hong Kong to start from scratch, where he meets a benefactor and earns his first million in his life. Under the guidance of a mentor, he goes to Beijing and becomes a well-known investor. Soon Gao Shan meets Tsin Vo, who became a financial headhunter. Can love help them find their way to each other again? ‎<br /><br />‎Based on the novel by Xiao Moli "Little Storm 1.0"‎</p>2
 
2.2%
<p>A story that follows two people's brave pursuit of love from their campus days to their humble beginnings as they enter the workplace to chase after their dreams together.</p>2
 
2.2%
<p>A story that follows undercover cop Gan Tian Lei who spent 10 years of his life walking a gray area. After waking up from a serious injury, he restarts his life and tries to solve a case by relying on his lost memories.</p>2
 
2.2%
<p>A daring, funny, and brutally honest show that covers politics, entertainment, movies, sports, and pop culture.</p>2
 
2.2%
<p>Abgründe des Privatlebens, das Scheitern in Serie - Christian Ulmen und Fahri Yardim kämpfen sich in jerks. von einer erschütternden Peinlichkeit in die nächste. jerks. erzählt das ganz normale Leben der beiden Filmstars. Aus kleinen Fehlern werden große Krisen, keine Minderheit, die nicht aus Versehen beleidigt wird. Direkt, schonungslos, manchmal unerträglich: Mit improvisierten Dialogen erzeugt jerks. den Eindruck einer Dokumentation, der der Zuschauer als peinlich berührter Zeuge beiwohnt.</p>2
 
2.2%
Other values (46)49
54.4%
(Missing)9
 
10.0%

Length

2022-09-04T23:44:34.637755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the247
 
5.3%
and167
 
3.6%
a139
 
3.0%
to134
 
2.9%
of117
 
2.5%
in116
 
2.5%
is42
 
0.9%
who41
 
0.9%
on39
 
0.8%
his38
 
0.8%
Other values (1397)3554
76.7%

Most occurring characters

ValueCountFrequency (%)
4546
16.4%
e2656
 
9.6%
a1671
 
6.0%
t1666
 
6.0%
n1632
 
5.9%
o1617
 
5.8%
i1481
 
5.3%
s1396
 
5.0%
r1298
 
4.7%
h1047
 
3.8%
Other values (87)8776
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter20831
75.0%
Space Separator4553
 
16.4%
Uppercase Letter850
 
3.1%
Other Punctuation742
 
2.7%
Math Symbol536
 
1.9%
Decimal Number159
 
0.6%
Dash Punctuation90
 
0.3%
Format12
 
< 0.1%
Open Punctuation6
 
< 0.1%
Close Punctuation6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2656
12.8%
a1671
 
8.0%
t1666
 
8.0%
n1632
 
7.8%
o1617
 
7.8%
i1481
 
7.1%
s1396
 
6.7%
r1298
 
6.2%
h1047
 
5.0%
l858
 
4.1%
Other values (30)5509
26.4%
Uppercase Letter
ValueCountFrequency (%)
S94
 
11.1%
H74
 
8.7%
T59
 
6.9%
A47
 
5.5%
W47
 
5.5%
D47
 
5.5%
F44
 
5.2%
Y39
 
4.6%
M38
 
4.5%
J37
 
4.4%
Other values (17)324
38.1%
Other Punctuation
ValueCountFrequency (%)
.254
34.2%
,230
31.0%
/143
19.3%
'41
 
5.5%
%24
 
3.2%
?19
 
2.6%
"18
 
2.4%
:7
 
0.9%
!5
 
0.7%
1
 
0.1%
Decimal Number
ValueCountFrequency (%)
064
40.3%
231
19.5%
122
 
13.8%
416
 
10.1%
510
 
6.3%
98
 
5.0%
85
 
3.1%
72
 
1.3%
31
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
-82
91.1%
7
 
7.8%
1
 
1.1%
Space Separator
ValueCountFrequency (%)
4546
99.8%
 7
 
0.2%
Math Symbol
ValueCountFrequency (%)
<268
50.0%
>268
50.0%
Format
ValueCountFrequency (%)
12
100.0%
Open Punctuation
ValueCountFrequency (%)
(6
100.0%
Close Punctuation
ValueCountFrequency (%)
)6
100.0%
Currency Symbol
ValueCountFrequency (%)
$1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21670
78.0%
Common6105
 
22.0%
Cyrillic11
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2656
12.3%
a1671
 
7.7%
t1666
 
7.7%
n1632
 
7.5%
o1617
 
7.5%
i1481
 
6.8%
s1396
 
6.4%
r1298
 
6.0%
h1047
 
4.8%
l858
 
4.0%
Other values (47)6348
29.3%
Common
ValueCountFrequency (%)
4546
74.5%
<268
 
4.4%
>268
 
4.4%
.254
 
4.2%
,230
 
3.8%
/143
 
2.3%
-82
 
1.3%
064
 
1.0%
'41
 
0.7%
231
 
0.5%
Other values (20)178
 
2.9%
Cyrillic
ValueCountFrequency (%)
а2
18.2%
М1
9.1%
у1
9.1%
ж1
9.1%
с1
9.1%
к1
9.1%
я1
9.1%
т1
9.1%
е1
9.1%
м1
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII27729
99.8%
None25
 
0.1%
Punctuation21
 
0.1%
Cyrillic11
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4546
16.4%
e2656
 
9.6%
a1671
 
6.0%
t1666
 
6.0%
n1632
 
5.9%
o1617
 
5.8%
i1481
 
5.3%
s1396
 
5.0%
r1298
 
4.7%
h1047
 
3.8%
Other values (67)8719
31.4%
Punctuation
ValueCountFrequency (%)
12
57.1%
7
33.3%
1
 
4.8%
1
 
4.8%
None
ValueCountFrequency (%)
ä8
32.0%
 7
28.0%
ü6
24.0%
ß2
 
8.0%
å1
 
4.0%
é1
 
4.0%
Cyrillic
ValueCountFrequency (%)
а2
18.2%
М1
9.1%
у1
9.1%
ж1
9.1%
с1
9.1%
к1
9.1%
я1
9.1%
т1
9.1%
е1
9.1%
м1
9.1%

_embedded.show.updated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct65
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1637637580
Minimum1609060726
Maximum1662346277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-09-04T23:44:34.737924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1609060726
5-th percentile1611024818
Q11613509592
median1643944724
Q31656357007
95-th percentile1662217986
Maximum1662346277
Range53285551
Interquartile range (IQR)42847415.25

Descriptive statistics

Standard deviation20249466.87
Coefficient of variation (CV)0.01236504774
Kurtosis-1.631026313
Mean1637637580
Median Absolute Deviation (MAD)18273235
Skewness-0.1765219413
Sum1.473873822 × 1011
Variance4.100409085 × 1014
MonotonicityNot monotonic
2022-09-04T23:44:34.830925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16115091688
 
8.9%
16622179868
 
8.9%
16442552222
 
2.2%
16357351792
 
2.2%
16128425832
 
2.2%
16196334992
 
2.2%
16124781452
 
2.2%
16549764112
 
2.2%
16481900582
 
2.2%
16095351412
 
2.2%
Other values (55)58
64.4%
ValueCountFrequency (%)
16090607262
 
2.2%
16095351412
 
2.2%
16108903401
 
1.1%
16111891791
 
1.1%
16114368421
 
1.1%
16115091688
8.9%
16120078311
 
1.1%
16123781171
 
1.1%
16124781452
 
2.2%
16124799201
 
1.1%
ValueCountFrequency (%)
16623462771
 
1.1%
16622756681
 
1.1%
16622629611
 
1.1%
16622179868
8.9%
16622179311
 
1.1%
16620501331
 
1.1%
16619691591
 
1.1%
16618725611
 
1.1%
16616736371
 
1.1%
16614857291
 
1.1%

_embedded.show._links.self.href
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct65
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Memory size848.0 B
https://api.tvmaze.com/shows/53101
https://api.tvmaze.com/shows/54664
https://api.tvmaze.com/shows/21735
 
2
https://api.tvmaze.com/shows/58689
 
2
https://api.tvmaze.com/shows/52400
 
2
Other values (60)
68 

Length

Max length34
Median length34
Mean length33.96666667
Min length33

Characters and Unicode

Total characters3057
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)57.8%

Sample

1st rowhttps://api.tvmaze.com/shows/49630
2nd rowhttps://api.tvmaze.com/shows/52520
3rd rowhttps://api.tvmaze.com/shows/49206
4th rowhttps://api.tvmaze.com/shows/49652
5th rowhttps://api.tvmaze.com/shows/44276

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/shows/531018
 
8.9%
https://api.tvmaze.com/shows/546648
 
8.9%
https://api.tvmaze.com/shows/217352
 
2.2%
https://api.tvmaze.com/shows/586892
 
2.2%
https://api.tvmaze.com/shows/524002
 
2.2%
https://api.tvmaze.com/shows/524212
 
2.2%
https://api.tvmaze.com/shows/525242
 
2.2%
https://api.tvmaze.com/shows/527432
 
2.2%
https://api.tvmaze.com/shows/152502
 
2.2%
https://api.tvmaze.com/shows/521042
 
2.2%
Other values (55)58
64.4%

Length

2022-09-04T23:44:34.920063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/shows/531018
 
8.9%
https://api.tvmaze.com/shows/546648
 
8.9%
https://api.tvmaze.com/shows/527432
 
2.2%
https://api.tvmaze.com/shows/521592
 
2.2%
https://api.tvmaze.com/shows/534582
 
2.2%
https://api.tvmaze.com/shows/521042
 
2.2%
https://api.tvmaze.com/shows/152502
 
2.2%
https://api.tvmaze.com/shows/627642
 
2.2%
https://api.tvmaze.com/shows/525242
 
2.2%
https://api.tvmaze.com/shows/524212
 
2.2%
Other values (55)58
64.4%

Most occurring characters

ValueCountFrequency (%)
/360
 
11.8%
s270
 
8.8%
t270
 
8.8%
h180
 
5.9%
p180
 
5.9%
a180
 
5.9%
o180
 
5.9%
.180
 
5.9%
m180
 
5.9%
e90
 
2.9%
Other values (16)987
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1980
64.8%
Other Punctuation630
 
20.6%
Decimal Number447
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s270
13.6%
t270
13.6%
h180
9.1%
p180
9.1%
a180
9.1%
o180
9.1%
m180
9.1%
e90
 
4.5%
w90
 
4.5%
c90
 
4.5%
Other values (3)270
13.6%
Decimal Number
ValueCountFrequency (%)
583
18.6%
467
15.0%
255
12.3%
649
11.0%
147
10.5%
735
7.8%
033
 
7.4%
332
 
7.2%
827
 
6.0%
919
 
4.3%
Other Punctuation
ValueCountFrequency (%)
/360
57.1%
.180
28.6%
:90
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1980
64.8%
Common1077
35.2%

Most frequent character per script

Common
ValueCountFrequency (%)
/360
33.4%
.180
16.7%
:90
 
8.4%
583
 
7.7%
467
 
6.2%
255
 
5.1%
649
 
4.5%
147
 
4.4%
735
 
3.2%
033
 
3.1%
Other values (3)78
 
7.2%
Latin
ValueCountFrequency (%)
s270
13.6%
t270
13.6%
h180
9.1%
p180
9.1%
a180
9.1%
o180
9.1%
m180
9.1%
e90
 
4.5%
w90
 
4.5%
c90
 
4.5%
Other values (3)270
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3057
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/360
 
11.8%
s270
 
8.8%
t270
 
8.8%
h180
 
5.9%
p180
 
5.9%
a180
 
5.9%
o180
 
5.9%
.180
 
5.9%
m180
 
5.9%
e90
 
2.9%
Other values (16)987
32.3%

_embedded.show._links.previousepisode.href
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct65
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Memory size848.0 B
https://api.tvmaze.com/episodes/2015719
https://api.tvmaze.com/episodes/2068324
https://api.tvmaze.com/episodes/2178740
 
2
https://api.tvmaze.com/episodes/2205983
 
2
https://api.tvmaze.com/episodes/1984963
 
2
Other values (60)
68 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters3510
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)57.8%

Sample

1st rowhttps://api.tvmaze.com/episodes/2380515
2nd rowhttps://api.tvmaze.com/episodes/1988016
3rd rowhttps://api.tvmaze.com/episodes/2386129
4th rowhttps://api.tvmaze.com/episodes/2374448
5th rowhttps://api.tvmaze.com/episodes/1993665

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/20157198
 
8.9%
https://api.tvmaze.com/episodes/20683248
 
8.9%
https://api.tvmaze.com/episodes/21787402
 
2.2%
https://api.tvmaze.com/episodes/22059832
 
2.2%
https://api.tvmaze.com/episodes/19849632
 
2.2%
https://api.tvmaze.com/episodes/19854962
 
2.2%
https://api.tvmaze.com/episodes/19880792
 
2.2%
https://api.tvmaze.com/episodes/19975522
 
2.2%
https://api.tvmaze.com/episodes/23012762
 
2.2%
https://api.tvmaze.com/episodes/19760542
 
2.2%
Other values (55)58
64.4%

Length

2022-09-04T23:44:34.992063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/20157198
 
8.9%
https://api.tvmaze.com/episodes/20683248
 
8.9%
https://api.tvmaze.com/episodes/19975522
 
2.2%
https://api.tvmaze.com/episodes/19776512
 
2.2%
https://api.tvmaze.com/episodes/20291112
 
2.2%
https://api.tvmaze.com/episodes/19760542
 
2.2%
https://api.tvmaze.com/episodes/23012762
 
2.2%
https://api.tvmaze.com/episodes/23539192
 
2.2%
https://api.tvmaze.com/episodes/19880792
 
2.2%
https://api.tvmaze.com/episodes/19854962
 
2.2%
Other values (55)58
64.4%

Most occurring characters

ValueCountFrequency (%)
/360
 
10.3%
t270
 
7.7%
p270
 
7.7%
s270
 
7.7%
e270
 
7.7%
a180
 
5.1%
i180
 
5.1%
.180
 
5.1%
m180
 
5.1%
o180
 
5.1%
Other values (16)1170
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2250
64.1%
Other Punctuation630
 
17.9%
Decimal Number630
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t270
12.0%
p270
12.0%
s270
12.0%
e270
12.0%
a180
8.0%
i180
8.0%
m180
8.0%
o180
8.0%
h90
 
4.0%
d90
 
4.0%
Other values (3)270
12.0%
Decimal Number
ValueCountFrequency (%)
2116
18.4%
185
13.5%
968
10.8%
065
10.3%
357
9.0%
552
8.3%
749
7.8%
849
7.8%
646
 
7.3%
443
 
6.8%
Other Punctuation
ValueCountFrequency (%)
/360
57.1%
.180
28.6%
:90
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2250
64.1%
Common1260
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/360
28.6%
.180
14.3%
2116
 
9.2%
:90
 
7.1%
185
 
6.7%
968
 
5.4%
065
 
5.2%
357
 
4.5%
552
 
4.1%
749
 
3.9%
Other values (3)138
 
11.0%
Latin
ValueCountFrequency (%)
t270
12.0%
p270
12.0%
s270
12.0%
e270
12.0%
a180
8.0%
i180
8.0%
m180
8.0%
o180
8.0%
h90
 
4.0%
d90
 
4.0%
Other values (3)270
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/360
 
10.3%
t270
 
7.7%
p270
 
7.7%
s270
 
7.7%
e270
 
7.7%
a180
 
5.1%
i180
 
5.1%
.180
 
5.1%
m180
 
5.1%
o180
 
5.1%
Other values (16)1170
33.3%

image
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing90
Missing (%)100.0%
Memory size848.0 B

_embedded.show.webChannel.country.name
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)17.5%
Missing33
Missing (%)36.7%
Memory size848.0 B
Korea, Republic of
21 
China
17 
United States
Norway
United Kingdom
Other values (5)

Length

Max length25
Median length18
Mean length12.12280702
Min length5

Characters and Unicode

Total characters691
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)5.3%

Sample

1st rowRussian Federation
2nd rowChina
3rd rowChina
4th rowKorea, Republic of
5th rowKorea, Republic of

Common Values

ValueCountFrequency (%)
Korea, Republic of21
23.3%
China17
18.9%
United States5
 
5.6%
Norway4
 
4.4%
United Kingdom3
 
3.3%
Taiwan, Province of China2
 
2.2%
Germany2
 
2.2%
Russian Federation1
 
1.1%
Kazakhstan1
 
1.1%
Japan1
 
1.1%
(Missing)33
36.7%

Length

2022-09-04T23:44:35.071063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:35.166062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
of23
20.2%
korea21
18.4%
republic21
18.4%
china19
16.7%
united8
 
7.0%
states5
 
4.4%
norway4
 
3.5%
kingdom3
 
2.6%
taiwan2
 
1.8%
province2
 
1.8%
Other values (5)6
 
5.3%

Most occurring characters

ValueCountFrequency (%)
a62
 
9.0%
e61
 
8.8%
57
 
8.2%
i57
 
8.2%
o54
 
7.8%
n40
 
5.8%
r30
 
4.3%
K25
 
3.6%
f23
 
3.3%
,23
 
3.3%
Other values (26)259
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter520
75.3%
Uppercase Letter91
 
13.2%
Space Separator57
 
8.2%
Other Punctuation23
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a62
11.9%
e61
11.7%
i57
11.0%
o54
10.4%
n40
 
7.7%
r30
 
5.8%
f23
 
4.4%
c23
 
4.4%
p22
 
4.2%
u22
 
4.2%
Other values (13)126
24.2%
Uppercase Letter
ValueCountFrequency (%)
K25
27.5%
R22
24.2%
C19
20.9%
U8
 
8.8%
S5
 
5.5%
N4
 
4.4%
T2
 
2.2%
P2
 
2.2%
G2
 
2.2%
F1
 
1.1%
Space Separator
ValueCountFrequency (%)
57
100.0%
Other Punctuation
ValueCountFrequency (%)
,23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin611
88.4%
Common80
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a62
 
10.1%
e61
 
10.0%
i57
 
9.3%
o54
 
8.8%
n40
 
6.5%
r30
 
4.9%
K25
 
4.1%
f23
 
3.8%
c23
 
3.8%
R22
 
3.6%
Other values (24)214
35.0%
Common
ValueCountFrequency (%)
57
71.2%
,23
28.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a62
 
9.0%
e61
 
8.8%
57
 
8.2%
i57
 
8.2%
o54
 
7.8%
n40
 
5.8%
r30
 
4.3%
K25
 
3.6%
f23
 
3.3%
,23
 
3.3%
Other values (26)259
37.5%

_embedded.show.webChannel.country.code
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)17.5%
Missing33
Missing (%)36.7%
Memory size848.0 B
KR
21 
CN
17 
US
NO
GB
Other values (5)

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters114
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)5.3%

Sample

1st rowRU
2nd rowCN
3rd rowCN
4th rowKR
5th rowKR

Common Values

ValueCountFrequency (%)
KR21
23.3%
CN17
18.9%
US5
 
5.6%
NO4
 
4.4%
GB3
 
3.3%
TW2
 
2.2%
DE2
 
2.2%
RU1
 
1.1%
KZ1
 
1.1%
JP1
 
1.1%
(Missing)33
36.7%

Length

2022-09-04T23:44:35.290907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:35.375907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
kr21
36.8%
cn17
29.8%
us5
 
8.8%
no4
 
7.0%
gb3
 
5.3%
tw2
 
3.5%
de2
 
3.5%
ru1
 
1.8%
kz1
 
1.8%
jp1
 
1.8%

Most occurring characters

ValueCountFrequency (%)
K22
19.3%
R22
19.3%
N21
18.4%
C17
14.9%
U6
 
5.3%
S5
 
4.4%
O4
 
3.5%
G3
 
2.6%
B3
 
2.6%
T2
 
1.8%
Other values (6)9
7.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter114
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K22
19.3%
R22
19.3%
N21
18.4%
C17
14.9%
U6
 
5.3%
S5
 
4.4%
O4
 
3.5%
G3
 
2.6%
B3
 
2.6%
T2
 
1.8%
Other values (6)9
7.9%

Most occurring scripts

ValueCountFrequency (%)
Latin114
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
K22
19.3%
R22
19.3%
N21
18.4%
C17
14.9%
U6
 
5.3%
S5
 
4.4%
O4
 
3.5%
G3
 
2.6%
B3
 
2.6%
T2
 
1.8%
Other values (6)9
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K22
19.3%
R22
19.3%
N21
18.4%
C17
14.9%
U6
 
5.3%
S5
 
4.4%
O4
 
3.5%
G3
 
2.6%
B3
 
2.6%
T2
 
1.8%
Other values (6)9
7.9%

_embedded.show.webChannel.country.timezone
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)17.5%
Missing33
Missing (%)36.7%
Memory size848.0 B
Asia/Seoul
21 
Asia/Shanghai
17 
America/New_York
Europe/Oslo
Europe/London
Other values (5)

Length

Max length16
Median length15
Mean length12
Min length10

Characters and Unicode

Total characters684
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)5.3%

Sample

1st rowAsia/Kamchatka
2nd rowAsia/Shanghai
3rd rowAsia/Shanghai
4th rowAsia/Seoul
5th rowAsia/Seoul

Common Values

ValueCountFrequency (%)
Asia/Seoul21
23.3%
Asia/Shanghai17
18.9%
America/New_York5
 
5.6%
Europe/Oslo4
 
4.4%
Europe/London3
 
3.3%
Asia/Taipei2
 
2.2%
Europe/Busingen2
 
2.2%
Asia/Kamchatka1
 
1.1%
Asia/Qyzylorda1
 
1.1%
Asia/Tokyo1
 
1.1%
(Missing)33
36.7%

Length

2022-09-04T23:44:35.464907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:35.565172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
asia/seoul21
36.8%
asia/shanghai17
29.8%
america/new_york5
 
8.8%
europe/oslo4
 
7.0%
europe/london3
 
5.3%
asia/taipei2
 
3.5%
europe/busingen2
 
3.5%
asia/kamchatka1
 
1.8%
asia/qyzylorda1
 
1.8%
asia/tokyo1
 
1.8%

Most occurring characters

ValueCountFrequency (%)
a88
12.9%
i71
10.4%
/57
 
8.3%
s49
 
7.2%
A48
 
7.0%
o48
 
7.0%
e44
 
6.4%
S38
 
5.6%
h35
 
5.1%
u32
 
4.7%
Other values (23)174
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter503
73.5%
Uppercase Letter119
 
17.4%
Other Punctuation57
 
8.3%
Connector Punctuation5
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a88
17.5%
i71
14.1%
s49
9.7%
o48
9.5%
e44
8.7%
h35
 
7.0%
u32
 
6.4%
n27
 
5.4%
l26
 
5.2%
r20
 
4.0%
Other values (10)63
12.5%
Uppercase Letter
ValueCountFrequency (%)
A48
40.3%
S38
31.9%
E9
 
7.6%
N5
 
4.2%
Y5
 
4.2%
O4
 
3.4%
L3
 
2.5%
T3
 
2.5%
B2
 
1.7%
K1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
/57
100.0%
Connector Punctuation
ValueCountFrequency (%)
_5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin622
90.9%
Common62
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a88
14.1%
i71
11.4%
s49
 
7.9%
A48
 
7.7%
o48
 
7.7%
e44
 
7.1%
S38
 
6.1%
h35
 
5.6%
u32
 
5.1%
n27
 
4.3%
Other values (21)142
22.8%
Common
ValueCountFrequency (%)
/57
91.9%
_5
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a88
12.9%
i71
10.4%
/57
 
8.3%
s49
 
7.2%
A48
 
7.0%
o48
 
7.0%
e44
 
6.4%
S38
 
5.6%
h35
 
5.1%
u32
 
4.7%
Other values (23)174
25.4%

_embedded.show._links.nextepisode.href
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct4
Distinct (%)100.0%
Missing86
Missing (%)95.6%
Memory size848.0 B
https://api.tvmaze.com/episodes/2374449
https://api.tvmaze.com/episodes/2376728
https://api.tvmaze.com/episodes/2379702
https://api.tvmaze.com/episodes/2367107

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters156
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/2374449
2nd rowhttps://api.tvmaze.com/episodes/2376728
3rd rowhttps://api.tvmaze.com/episodes/2379702
4th rowhttps://api.tvmaze.com/episodes/2367107

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/23744491
 
1.1%
https://api.tvmaze.com/episodes/23767281
 
1.1%
https://api.tvmaze.com/episodes/23797021
 
1.1%
https://api.tvmaze.com/episodes/23671071
 
1.1%
(Missing)86
95.6%

Length

2022-09-04T23:44:35.667381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:35.881537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/23744491
25.0%
https://api.tvmaze.com/episodes/23767281
25.0%
https://api.tvmaze.com/episodes/23797021
25.0%
https://api.tvmaze.com/episodes/23671071
25.0%

Most occurring characters

ValueCountFrequency (%)
/16
 
10.3%
e12
 
7.7%
p12
 
7.7%
s12
 
7.7%
t12
 
7.7%
o8
 
5.1%
a8
 
5.1%
i8
 
5.1%
.8
 
5.1%
m8
 
5.1%
Other values (15)52
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter100
64.1%
Other Punctuation28
 
17.9%
Decimal Number28
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e12
12.0%
p12
12.0%
s12
12.0%
t12
12.0%
o8
8.0%
a8
8.0%
i8
8.0%
m8
8.0%
d4
 
4.0%
h4
 
4.0%
Other values (3)12
12.0%
Decimal Number
ValueCountFrequency (%)
77
25.0%
26
21.4%
34
14.3%
43
10.7%
92
 
7.1%
62
 
7.1%
02
 
7.1%
81
 
3.6%
11
 
3.6%
Other Punctuation
ValueCountFrequency (%)
/16
57.1%
.8
28.6%
:4
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin100
64.1%
Common56
35.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e12
12.0%
p12
12.0%
s12
12.0%
t12
12.0%
o8
8.0%
a8
8.0%
i8
8.0%
m8
8.0%
d4
 
4.0%
h4
 
4.0%
Other values (3)12
12.0%
Common
ValueCountFrequency (%)
/16
28.6%
.8
14.3%
77
12.5%
26
 
10.7%
34
 
7.1%
:4
 
7.1%
43
 
5.4%
92
 
3.6%
62
 
3.6%
02
 
3.6%
Other values (2)2
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/16
 
10.3%
e12
 
7.7%
p12
 
7.7%
s12
 
7.7%
t12
 
7.7%
o8
 
5.1%
a8
 
5.1%
i8
 
5.1%
.8
 
5.1%
m8
 
5.1%
Other values (15)52
33.3%

_embedded.show.network.id
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct5
Distinct (%)100.0%
Missing85
Missing (%)94.4%
Memory size848.0 B
1808.0
402.0
112.0
30.0
132.0

Length

Max length6
Median length5
Mean length5
Min length4

Characters and Unicode

Total characters25
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row1808.0
2nd row402.0
3rd row112.0
4th row30.0
5th row132.0

Common Values

ValueCountFrequency (%)
1808.01
 
1.1%
402.01
 
1.1%
112.01
 
1.1%
30.01
 
1.1%
132.01
 
1.1%
(Missing)85
94.4%

Length

2022-09-04T23:44:35.976799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:36.063948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1808.01
20.0%
402.01
20.0%
112.01
20.0%
30.01
20.0%
132.01
20.0%

Most occurring characters

ValueCountFrequency (%)
08
32.0%
.5
20.0%
14
16.0%
23
 
12.0%
82
 
8.0%
32
 
8.0%
41
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20
80.0%
Other Punctuation5
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08
40.0%
14
20.0%
23
 
15.0%
82
 
10.0%
32
 
10.0%
41
 
5.0%
Other Punctuation
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08
32.0%
.5
20.0%
14
16.0%
23
 
12.0%
82
 
8.0%
32
 
8.0%
41
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08
32.0%
.5
20.0%
14
16.0%
23
 
12.0%
82
 
8.0%
32
 
8.0%
41
 
4.0%

_embedded.show.network.name
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct5
Distinct (%)100.0%
Missing85
Missing (%)94.4%
Memory size848.0 B
MBC Masr
Новий Канал
RTL4
USA Network
Tokyo MX

Length

Max length11
Median length8
Mean length8.4
Min length4

Characters and Unicode

Total characters42
Distinct characters31
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowMBC Masr
2nd rowНовий Канал
3rd rowRTL4
4th rowUSA Network
5th rowTokyo MX

Common Values

ValueCountFrequency (%)
MBC Masr1
 
1.1%
Новий Канал1
 
1.1%
RTL41
 
1.1%
USA Network1
 
1.1%
Tokyo MX1
 
1.1%
(Missing)85
94.4%

Length

2022-09-04T23:44:36.146948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:36.235405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
mbc1
11.1%
masr1
11.1%
новий1
11.1%
канал1
11.1%
rtl41
11.1%
usa1
11.1%
network1
11.1%
tokyo1
11.1%
mx1
11.1%

Most occurring characters

ValueCountFrequency (%)
4
 
9.5%
M3
 
7.1%
o3
 
7.1%
k2
 
4.8%
r2
 
4.8%
T2
 
4.8%
а2
 
4.8%
A1
 
2.4%
41
 
2.4%
U1
 
2.4%
Other values (21)21
50.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21
50.0%
Uppercase Letter16
38.1%
Space Separator4
 
9.5%
Decimal Number1
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o3
14.3%
k2
 
9.5%
r2
 
9.5%
а2
 
9.5%
t1
 
4.8%
e1
 
4.8%
w1
 
4.8%
y1
 
4.8%
л1
 
4.8%
н1
 
4.8%
Other values (6)6
28.6%
Uppercase Letter
ValueCountFrequency (%)
M3
18.8%
T2
12.5%
A1
 
6.2%
U1
 
6.2%
S1
 
6.2%
N1
 
6.2%
L1
 
6.2%
R1
 
6.2%
B1
 
6.2%
К1
 
6.2%
Other values (3)3
18.8%
Space Separator
ValueCountFrequency (%)
4
100.0%
Decimal Number
ValueCountFrequency (%)
41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin27
64.3%
Cyrillic10
 
23.8%
Common5
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
M3
 
11.1%
o3
 
11.1%
k2
 
7.4%
r2
 
7.4%
T2
 
7.4%
A1
 
3.7%
U1
 
3.7%
S1
 
3.7%
t1
 
3.7%
N1
 
3.7%
Other values (10)10
37.0%
Cyrillic
ValueCountFrequency (%)
а2
20.0%
л1
10.0%
н1
10.0%
К1
10.0%
й1
10.0%
и1
10.0%
в1
10.0%
о1
10.0%
Н1
10.0%
Common
ValueCountFrequency (%)
4
80.0%
41
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII32
76.2%
Cyrillic10
 
23.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4
 
12.5%
M3
 
9.4%
o3
 
9.4%
k2
 
6.2%
r2
 
6.2%
T2
 
6.2%
A1
 
3.1%
41
 
3.1%
U1
 
3.1%
S1
 
3.1%
Other values (12)12
37.5%
Cyrillic
ValueCountFrequency (%)
а2
20.0%
л1
10.0%
н1
10.0%
К1
10.0%
й1
10.0%
и1
10.0%
в1
10.0%
о1
10.0%
Н1
10.0%

_embedded.show.network.country.name
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct5
Distinct (%)100.0%
Missing85
Missing (%)94.4%
Memory size848.0 B
Egypt
Ukraine
Netherlands
United States
Japan

Length

Max length13
Median length11
Mean length8.2
Min length5

Characters and Unicode

Total characters41
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowEgypt
2nd rowUkraine
3rd rowNetherlands
4th rowUnited States
5th rowJapan

Common Values

ValueCountFrequency (%)
Egypt1
 
1.1%
Ukraine1
 
1.1%
Netherlands1
 
1.1%
United States1
 
1.1%
Japan1
 
1.1%
(Missing)85
94.4%

Length

2022-09-04T23:44:36.323405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:36.408340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
egypt1
16.7%
ukraine1
16.7%
netherlands1
16.7%
united1
16.7%
states1
16.7%
japan1
16.7%

Most occurring characters

ValueCountFrequency (%)
t5
12.2%
a5
12.2%
e5
12.2%
n4
 
9.8%
p2
 
4.9%
U2
 
4.9%
r2
 
4.9%
i2
 
4.9%
s2
 
4.9%
d2
 
4.9%
Other values (10)10
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter34
82.9%
Uppercase Letter6
 
14.6%
Space Separator1
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t5
14.7%
a5
14.7%
e5
14.7%
n4
11.8%
p2
 
5.9%
r2
 
5.9%
i2
 
5.9%
s2
 
5.9%
d2
 
5.9%
l1
 
2.9%
Other values (4)4
11.8%
Uppercase Letter
ValueCountFrequency (%)
U2
33.3%
S1
16.7%
E1
16.7%
N1
16.7%
J1
16.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin40
97.6%
Common1
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t5
12.5%
a5
12.5%
e5
12.5%
n4
10.0%
p2
 
5.0%
U2
 
5.0%
r2
 
5.0%
i2
 
5.0%
s2
 
5.0%
d2
 
5.0%
Other values (9)9
22.5%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII41
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t5
12.2%
a5
12.2%
e5
12.2%
n4
 
9.8%
p2
 
4.9%
U2
 
4.9%
r2
 
4.9%
i2
 
4.9%
s2
 
4.9%
d2
 
4.9%
Other values (10)10
24.4%

_embedded.show.network.country.code
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct5
Distinct (%)100.0%
Missing85
Missing (%)94.4%
Memory size848.0 B
EG
UA
NL
US
JP

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowEG
2nd rowUA
3rd rowNL
4th rowUS
5th rowJP

Common Values

ValueCountFrequency (%)
EG1
 
1.1%
UA1
 
1.1%
NL1
 
1.1%
US1
 
1.1%
JP1
 
1.1%
(Missing)85
94.4%

Length

2022-09-04T23:44:36.481337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:36.556506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
eg1
20.0%
ua1
20.0%
nl1
20.0%
us1
20.0%
jp1
20.0%

Most occurring characters

ValueCountFrequency (%)
U2
20.0%
E1
10.0%
G1
10.0%
A1
10.0%
N1
10.0%
L1
10.0%
S1
10.0%
J1
10.0%
P1
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U2
20.0%
E1
10.0%
G1
10.0%
A1
10.0%
N1
10.0%
L1
10.0%
S1
10.0%
J1
10.0%
P1
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U2
20.0%
E1
10.0%
G1
10.0%
A1
10.0%
N1
10.0%
L1
10.0%
S1
10.0%
J1
10.0%
P1
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U2
20.0%
E1
10.0%
G1
10.0%
A1
10.0%
N1
10.0%
L1
10.0%
S1
10.0%
J1
10.0%
P1
10.0%

_embedded.show.network.country.timezone
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct5
Distinct (%)100.0%
Missing85
Missing (%)94.4%
Memory size848.0 B
Africa/Cairo
Europe/Zaporozhye
Europe/Amsterdam
America/New_York
Asia/Tokyo

Length

Max length17
Median length16
Mean length14.2
Min length10

Characters and Unicode

Total characters71
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowAfrica/Cairo
2nd rowEurope/Zaporozhye
3rd rowEurope/Amsterdam
4th rowAmerica/New_York
5th rowAsia/Tokyo

Common Values

ValueCountFrequency (%)
Africa/Cairo1
 
1.1%
Europe/Zaporozhye1
 
1.1%
Europe/Amsterdam1
 
1.1%
America/New_York1
 
1.1%
Asia/Tokyo1
 
1.1%
(Missing)85
94.4%

Length

2022-09-04T23:44:36.632492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:36.709498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
africa/cairo1
20.0%
europe/zaporozhye1
20.0%
europe/amsterdam1
20.0%
america/new_york1
20.0%
asia/tokyo1
20.0%

Most occurring characters

ValueCountFrequency (%)
r8
 
11.3%
o8
 
11.3%
a6
 
8.5%
e6
 
8.5%
/5
 
7.0%
A4
 
5.6%
i4
 
5.6%
p3
 
4.2%
m3
 
4.2%
s2
 
2.8%
Other values (17)22
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter54
76.1%
Uppercase Letter11
 
15.5%
Other Punctuation5
 
7.0%
Connector Punctuation1
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r8
14.8%
o8
14.8%
a6
11.1%
e6
11.1%
i4
 
7.4%
p3
 
5.6%
m3
 
5.6%
s2
 
3.7%
y2
 
3.7%
u2
 
3.7%
Other values (8)10
18.5%
Uppercase Letter
ValueCountFrequency (%)
A4
36.4%
E2
18.2%
Y1
 
9.1%
N1
 
9.1%
Z1
 
9.1%
C1
 
9.1%
T1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
/5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin65
91.5%
Common6
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r8
12.3%
o8
12.3%
a6
 
9.2%
e6
 
9.2%
A4
 
6.2%
i4
 
6.2%
p3
 
4.6%
m3
 
4.6%
s2
 
3.1%
y2
 
3.1%
Other values (15)19
29.2%
Common
ValueCountFrequency (%)
/5
83.3%
_1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII71
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r8
 
11.3%
o8
 
11.3%
a6
 
8.5%
e6
 
8.5%
/5
 
7.0%
A4
 
5.6%
i4
 
5.6%
p3
 
4.2%
m3
 
4.2%
s2
 
2.8%
Other values (17)22
31.0%

_embedded.show.network.officialSite
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing90
Missing (%)100.0%
Memory size848.0 B

_embedded.show.image
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing90
Missing (%)100.0%
Memory size848.0 B

_embedded.show.dvdCountry.name
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing89
Missing (%)98.9%
Memory size848.0 B
Ukraine

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowUkraine

Common Values

ValueCountFrequency (%)
Ukraine1
 
1.1%
(Missing)89
98.9%

Length

2022-09-04T23:44:36.783491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:36.846494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ukraine1
100.0%

Most occurring characters

ValueCountFrequency (%)
U1
14.3%
k1
14.3%
r1
14.3%
a1
14.3%
i1
14.3%
n1
14.3%
e1
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6
85.7%
Uppercase Letter1
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
k1
16.7%
r1
16.7%
a1
16.7%
i1
16.7%
n1
16.7%
e1
16.7%
Uppercase Letter
ValueCountFrequency (%)
U1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U1
14.3%
k1
14.3%
r1
14.3%
a1
14.3%
i1
14.3%
n1
14.3%
e1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U1
14.3%
k1
14.3%
r1
14.3%
a1
14.3%
i1
14.3%
n1
14.3%
e1
14.3%

_embedded.show.dvdCountry.code
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing89
Missing (%)98.9%
Memory size848.0 B
UA

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowUA

Common Values

ValueCountFrequency (%)
UA1
 
1.1%
(Missing)89
98.9%

Length

2022-09-04T23:44:36.902492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:36.964494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ua1
100.0%

Most occurring characters

ValueCountFrequency (%)
U1
50.0%
A1
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U1
50.0%
A1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U1
50.0%
A1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U1
50.0%
A1
50.0%

_embedded.show.dvdCountry.timezone
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing89
Missing (%)98.9%
Memory size848.0 B
Europe/Zaporozhye

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters17
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowEurope/Zaporozhye

Common Values

ValueCountFrequency (%)
Europe/Zaporozhye1
 
1.1%
(Missing)89
98.9%

Length

2022-09-04T23:44:37.019491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:44:37.082491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
europe/zaporozhye1
100.0%

Most occurring characters

ValueCountFrequency (%)
o3
17.6%
r2
11.8%
p2
11.8%
e2
11.8%
E1
 
5.9%
u1
 
5.9%
/1
 
5.9%
Z1
 
5.9%
a1
 
5.9%
z1
 
5.9%
Other values (2)2
11.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14
82.4%
Uppercase Letter2
 
11.8%
Other Punctuation1
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o3
21.4%
r2
14.3%
p2
14.3%
e2
14.3%
u1
 
7.1%
a1
 
7.1%
z1
 
7.1%
h1
 
7.1%
y1
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
E1
50.0%
Z1
50.0%
Other Punctuation
ValueCountFrequency (%)
/1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16
94.1%
Common1
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o3
18.8%
r2
12.5%
p2
12.5%
e2
12.5%
E1
 
6.2%
u1
 
6.2%
Z1
 
6.2%
a1
 
6.2%
z1
 
6.2%
h1
 
6.2%
Common
ValueCountFrequency (%)
/1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o3
17.6%
r2
11.8%
p2
11.8%
e2
11.8%
E1
 
5.9%
u1
 
5.9%
/1
 
5.9%
Z1
 
5.9%
a1
 
5.9%
z1
 
5.9%
Other values (2)2
11.8%

_embedded.show.webChannel
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing90
Missing (%)100.0%
Memory size848.0 B

Interactions

2022-09-04T23:44:25.941624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:02.085143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:03.910372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:06.002210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:07.831962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:10.083668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:12.153860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:14.315185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:16.088902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:18.170620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:19.981600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:21.971336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:24.035655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:26.017624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:02.553259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:04.044442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:06.131946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:07.967556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:10.220371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:12.315978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:14.426257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:16.214883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:18.380455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:20.143301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:22.342580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:24.169115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:26.102983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:02.689351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:04.199487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:06.253642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:08.130387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:10.392590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:12.457556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:14.576923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:16.381247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:18.505785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:20.287572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:22.455692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:24.320463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:26.358321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:02.787416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:04.355365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:06.350816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:08.271547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:10.540726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:12.590652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:14.690040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:16.544656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:18.669082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:20.429490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:22.566536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:24.512304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:26.452495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:02.897519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:04.465386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:06.440076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:08.431648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:10.713401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:12.732873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:14.815089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:16.655160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:18.811085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:20.638582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:22.688715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:24.689567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:26.577071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:03.024890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:04.579186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:06.614715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:08.577159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:10.877781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:12.880785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:14.929800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:16.800421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:18.908190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:20.776255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:22.789828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:24.829099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:26.668391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:03.108759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:04.702997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:06.747043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:08.723723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:11.029565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:13.047763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:15.088612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:16.953223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:19.034341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:20.921738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:22.963150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:24.984799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:26.762834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:03.192683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:04.821124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:06.927683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:08.863757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:11.153586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:13.146987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:15.222404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:17.063468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:19.171699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:21.073737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:23.117662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:25.131607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:26.860481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:03.279897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:05.140189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:07.073107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:08.992633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:11.296812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:13.303178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:15.343837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:17.213926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:19.293092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:21.218097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:23.285522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:25.257575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:26.932069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:03.403075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:05.287485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:07.206148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:09.415262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:11.426834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:13.418260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:15.507731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:17.345342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:19.392086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:21.374227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:23.413197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:25.390586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:27.010629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:03.513073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:05.470048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:07.344457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:09.584365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:11.584148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:13.571528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:15.629626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:17.465776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:19.506453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:21.492861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:23.556025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:25.554587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:27.099854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:03.652987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:05.670707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:07.504333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:09.715719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:11.887693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:14.003639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:15.796355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:17.595682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:19.629031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:21.624379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:23.698167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:25.692723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:27.181706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:03.784782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:05.809334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:07.664685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:09.921314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:12.042405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:14.146528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:15.937075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:18.002782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:19.825308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:21.802123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:23.878144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:44:25.850011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-09-04T23:44:37.157502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-04T23:44:37.394945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-04T23:44:37.637321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-04T23:44:37.913421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-04T23:44:27.503837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-04T23:44:28.740968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-04T23:44:29.458345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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02179614https://www.tvmaze.com/episodes/2179614/kontakty-1x31-kontakty-v-telefone-eldara-dzarahova-morgenshtern-slava-marlow-basta-dana-poperecnyjКОНТАКТЫ в телефоне Эльдара Джарахова: Morgenshtern, Slava Marlow, Баста, Даня Поперечный131.0regular2020-12-2312:002020-12-23T00:00:00+00:0036.0NoneNaNhttps://static.tvmaze.com/uploads/images/medium_landscape/360/901424.jpghttps://static.tvmaze.com/uploads/images/original_untouched/360/901424.jpghttps://api.tvmaze.com/episodes/217961449630https://www.tvmaze.com/shows/49630/kontaktyКонтактыGame ShowRussian[]Running30.042.02019-04-03Nonehttps://www.youtube.com/playlist?list=PLZ1FUdedsrSJubWkFFh5vKHzawzQk1mYI[Monday]NaN20NaN21.0YouTubeNaNhttps://www.youtube.comNaNNaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/315/789854.jpghttps://static.tvmaze.com/uploads/images/original_untouched/315/789854.jpgNone1661485729https://api.tvmaze.com/shows/49630https://api.tvmaze.com/episodes/2380515NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
11988015https://www.tvmaze.com/episodes/1988015/muzskaa-tema-1x04-seria-4Серия 414.0regular2020-12-2312:002020-12-23T00:00:00+00:0030.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/198801552520https://www.tvmaze.com/shows/52520/muzskaa-temaМужская темаTalk ShowRussian[]Ended30.030.02020-12-172020-12-25https://www.ivi.ru/watch/muzhskaya-tema12:00[Monday, Tuesday, Wednesday, Thursday, Friday]NaN4NaN337.0iviNaNhttps://www.ivi.ru/NaNNaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/289/723328.jpghttps://static.tvmaze.com/uploads/images/original_untouched/289/723328.jpg<p><b>Мужская тема</b> is a symbiosis of talk shows and modern podcasts, where male celebrities answer questions that concern people in the XXI century. Bright representatives of show business, theater, pop, cinema, sports, as well as Internet stars meet in the barbershop. Here, on male territory, they can openly discuss a variety of topics, sometimes seriously, and sometimes with humor. This is a chance to see the idol in a confidential communication without notes, compare his opinion with your own and hear what men really talk about when there is not a single girl around.</p>1616722619https://api.tvmaze.com/shows/52520https://api.tvmaze.com/episodes/1988016NaNRussian FederationRUAsia/KamchatkaNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22386109https://www.tvmaze.com/episodes/2386109/xian-feng-jian-yu-lu-1x50-episode-50Episode 50150.0regular2020-12-2310:002020-12-23T02:00:00+00:008.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/238610949206https://www.tvmaze.com/shows/49206/xian-feng-jian-yu-luXian Feng Jian Yu LuAnimationChinese[Action, Anime, Fantasy, Supernatural]Running8.07.02020-07-11Nonehttps://v.qq.com/detail/m/mzc00200hc38s5x.html10:00[Wednesday, Saturday]NaN52NaN104.0Tencent QQNaNhttps://v.qq.com/NaNNaN386423.0Nonehttps://static.tvmaze.com/uploads/images/medium_portrait/270/675333.jpghttps://static.tvmaze.com/uploads/images/original_untouched/270/675333.jpg<p>In ancient Shenzhou, humans and demons had been in constant dispute for thousands of years. The demon princess from Tushan, Bai Binglan, and the human Zhang Kuangyun met each other due to a misunderstanding. In order to investigate the enemy country, Bai Binglan became Zhang Kuangyun's companion. As they travel together, Zhang Kuangyun discovers a conspiracy...</p>1662275668https://api.tvmaze.com/shows/49206https://api.tvmaze.com/episodes/2386129NaNChinaCNAsia/ShanghaiNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32095629https://www.tvmaze.com/episodes/2095629/yi-nian-yong-heng-1x22-episode-22Episode 22122.0regular2020-12-2310:002020-12-23T02:00:00+00:0019.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/209562949652https://www.tvmaze.com/shows/49652/yi-nian-yong-hengYi Nian Yong HengAnimationChinese[Comedy, Action, Anime, Fantasy]Running19.019.02020-08-12Nonehttps://v.qq.com/detail/w/ww18u675tfmhas6.html10:00[Wednesday]NaN56NaN104.0Tencent QQNaNhttps://v.qq.com/NaNNaN388680.0tt13695606https://static.tvmaze.com/uploads/images/medium_portrait/267/669816.jpghttps://static.tvmaze.com/uploads/images/original_untouched/267/669816.jpg<p>One will to create oceans. One will to summon the mulberry fields.<br /><br />One will to slaughter countless devils. One will to eradicate innumerable immortals.<br /><br />Only my will… is eternal.<br /><br />A Will Eternal tells the tale of Bai Xiaochun, an endearing but exasperating young man who is driven primarily by his fear of death and desire to live forever, but who deeply values friendship and family.<br /><br />(Source: Novel Updates)</p>1660662594https://api.tvmaze.com/shows/49652https://api.tvmaze.com/episodes/2374448NaNChinaCNAsia/Shanghaihttps://api.tvmaze.com/episodes/2374449NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
41993657https://www.tvmaze.com/episodes/1993657/7-days-of-romance-2x02-episode-2Episode 222.0regular2020-12-232020-12-23T03:00:00+00:0015.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/199365744276https://www.tvmaze.com/shows/44276/7-days-of-romance7 Days of RomanceScriptedKorean[Drama, Romance]EndedNaN15.02019-10-082021-01-20None[Tuesday, Wednesday]NaN79NaN380.0SeeznNaNhttps://www.seezntv.com/NaNNaN370873.0tt13423446https://static.tvmaze.com/uploads/images/medium_portrait/290/727378.jpghttps://static.tvmaze.com/uploads/images/original_untouched/290/727378.jpg<p>Da Eun works part-time and Kim Byul is an idol in her 5th years since debut. These two girls who look alike decide to change each other's lives just for 7 days. It tells the romantic encounters of these 2 girls.</p>1650033745https://api.tvmaze.com/shows/44276https://api.tvmaze.com/episodes/1993665NaNKorea, Republic ofKRAsia/SeoulNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
52015712https://www.tvmaze.com/episodes/2015712/half-fifty-1x01-episode-1Episode 111.0regular2020-12-232020-12-23T03:00:00+00:0017.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/201571253101https://www.tvmaze.com/shows/53101/half-fiftyHalf-FiftyScriptedKorean[Drama, Comedy]Ended17.017.02020-12-232020-12-23None[Wednesday]NaN32NaN114.0wavveNaNhttps://www.wavve.com/NaNNaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/294/735240.jpghttps://static.tvmaze.com/uploads/images/original_untouched/294/735240.jpg<p><b>Half-Fifty</b> is a comedy drama about youth and growth, and the series follows a group of 25-year-olds who end up in the world of YouTubers.</p>1611509168https://api.tvmaze.com/shows/53101https://api.tvmaze.com/episodes/2015719NaNKorea, Republic ofKRAsia/SeoulNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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82015715https://www.tvmaze.com/episodes/2015715/half-fifty-1x04-episode-4Episode 414.0regular2020-12-232020-12-23T03:00:00+00:0017.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/201571553101https://www.tvmaze.com/shows/53101/half-fiftyHalf-FiftyScriptedKorean[Drama, Comedy]Ended17.017.02020-12-232020-12-23None[Wednesday]NaN32NaN114.0wavveNaNhttps://www.wavve.com/NaNNaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/294/735240.jpghttps://static.tvmaze.com/uploads/images/original_untouched/294/735240.jpg<p><b>Half-Fifty</b> is a comedy drama about youth and growth, and the series follows a group of 25-year-olds who end up in the world of YouTubers.</p>1611509168https://api.tvmaze.com/shows/53101https://api.tvmaze.com/episodes/2015719NaNKorea, Republic ofKRAsia/SeoulNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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851983826https://www.tvmaze.com/episodes/1983826/ghosts-s02-special-the-ghost-of-christmasThe Ghost of Christmas2NaNsignificant_special2020-12-2320:302020-12-23T20:30:00+00:0030.0<p>It's Christmas at Button House, and Mike is determined to make it perfect for his family. Alison is also determined to deliver the best Christmas ever for the living and the dead, although the latter aren't filled with festive cheer. Christmas just isn't that much fun when you're deceased. But when Julian is confronted with past wrongs, he has a revelation that could help the ghosts rediscover the real message of Christmas.</p>8.2https://static.tvmaze.com/uploads/images/medium_landscape/288/720523.jpghttps://static.tvmaze.com/uploads/images/original_untouched/288/720523.jpghttps://api.tvmaze.com/episodes/198382638241https://www.tvmaze.com/shows/38241/ghostsGhostsScriptedEnglish[Comedy, Supernatural]Running30.030.02019-04-15Nonehttps://www.bbc.co.uk/programmes/m00049t920:30[Monday]7.698NaN26.0BBC iPlayerNaNhttps://www.bbc.co.uk/iplayerNaNNaN361701.0tt8594324https://static.tvmaze.com/uploads/images/medium_portrait/191/477889.jpghttps://static.tvmaze.com/uploads/images/original_untouched/191/477889.jpg<p>The crumbling country pile of Button Hall is home to numerous restless spirits who have died there over the centuries - each ghost very much a product of their time, resigned to squabbling with each other for eternity over the most inane of daily gripes. But their lives - or, rather, afterlives - are thrown into turmoil when a young urban couple - Alison and Mike - surprisingly inherit the peaceful derelict house and make plans to turn it into a bustling family hotel. As the ghosts attempt to oust the newcomers from their home, and Mike and Alison discover the true scale of the project they've taken on, fate conspires to trap both sides in an impossible house share, where every day is, literally, a matter of life and death.</p>1651525002https://api.tvmaze.com/shows/38241https://api.tvmaze.com/episodes/2228596NaNUnited KingdomGBEurope/LondonNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
861979338https://www.tvmaze.com/episodes/1979338/motherland-s02-special-christmas-specialChristmas Special2NaNsignificant_special2020-12-2321:002020-12-23T21:00:00+00:0030.0<p>In Motherland, Christmas comes but once a year, and thank God for that, because it's time for Amanda's annual festive soiree (dress code: tinsel and tiaras). Nothing says Christmas like evil Santa, a thirty foot Christmas tree, Anne's Christmas cocktails and very strict rules about where you can and can't drink mulled wine.</p>8.3https://static.tvmaze.com/uploads/images/medium_landscape/290/726151.jpghttps://static.tvmaze.com/uploads/images/original_untouched/290/726151.jpghttps://api.tvmaze.com/episodes/197933832680https://www.tvmaze.com/shows/32680/motherlandMotherlandScriptedEnglish[Comedy]To Be Determined30.030.02017-11-07Nonehttp://www.bbc.co.uk/programmes/p05j1jkp22:00[Monday]6.993NaN26.0BBC iPlayerNaNhttps://www.bbc.co.uk/iplayerNaNNaN317476.0tt6006350https://static.tvmaze.com/uploads/images/medium_portrait/396/990773.jpghttps://static.tvmaze.com/uploads/images/original_untouched/396/990773.jpg<p><b>Motherland</b> is a series all about navigating the trials and traumas of motherhood, looking at the competitive and unromantic sides of parenting.</p>1655560608https://api.tvmaze.com/shows/32680https://api.tvmaze.com/episodes/2089037NaNUnited KingdomGBEurope/LondonNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
871990502https://www.tvmaze.com/episodes/1990502/king-gary-s01-special-christmas-specialChristmas Special1NaNsignificant_special2020-12-2322:002020-12-23T22:00:00+00:0035.0<p>In this festive special, Gray is looking forward to the Butterchurn Crescent Christmas lights display, which he hopes will brighten up the end of a difficult year - only for the event to be cancelled due to expense. Having landed a lucrative new contract at work, he takes it upon himself to save the lights - and Christmas.</p>3.7https://static.tvmaze.com/uploads/images/medium_landscape/290/726476.jpghttps://static.tvmaze.com/uploads/images/original_untouched/290/726476.jpghttps://api.tvmaze.com/episodes/199050245487https://www.tvmaze.com/shows/45487/king-garyKing GaryScriptedEnglish[Comedy]Running30.030.02020-01-10Nonehttps://www.bbc.co.uk/programmes/p07w768621:30[Friday]4.046NaN26.0BBC iPlayerNaNhttps://www.bbc.co.uk/iplayerNaNNaN356587.0tt9548664https://static.tvmaze.com/uploads/images/medium_portrait/235/588171.jpghttps://static.tvmaze.com/uploads/images/original_untouched/235/588171.jpg<p><b>King Gary</b> follows Gary King and love-of-his life, Terri as they bowl through family-life in suburbia. Gary's quest to impress the neighbours and fill dad, Big Gary's big shoes, might be more successful if he wasn't such a drama-queen but there's always a lot of love around in Butterchurn Crescent.</p>1638563133https://api.tvmaze.com/shows/45487https://api.tvmaze.com/episodes/2134345NaNUnited KingdomGBEurope/LondonNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
881958868https://www.tvmaze.com/episodes/1958868/wwe-nxt-14x52-main-event-adam-cole-vs-velveteen-dreamMain Event: Adam Cole vs. Velveteen Dream1452.0regular2020-12-2320:002020-12-24T01:00:00+00:00126.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/19588682266https://www.tvmaze.com/shows/2266/wwe-nxtWWE NXTSportsEnglish[]Running120.077.02010-02-23Nonehttp://www.wwe.com/inside/networkschedule20:00[Tuesday]7.290NaN15.0WWE NetworkNaNNoneNaN25100.0144541.0tt1601141https://static.tvmaze.com/uploads/images/medium_portrait/401/1002762.jpghttps://static.tvmaze.com/uploads/images/original_untouched/401/1002762.jpg<p>Each Wednesday at 8:00 p.m. ET, WWE Superstars and Divas of tomorrow face off on <b>WWE NXT</b><i>,</i> a one-hour weekly show that features the brightest and best of WWE's rising stars. WWE NXT showcases the Superstars and Divas from WWE's Performance Center as well as appearances from WWE Superstars and Legends in an intimate setting. WWE NXT broadcasts from the state-of-the-art Full Sail LIVE venue on the Full Sail University in campus in Orlando, Florida.</p>1661969159https://api.tvmaze.com/shows/2266https://api.tvmaze.com/episodes/2383154NaNUnited StatesUSAmerica/New_Yorkhttps://api.tvmaze.com/episodes/236710730.0USA NetworkUnited StatesUSAmerica/New_YorkNaNNaNNaNNaNNaNNaN
891945147https://www.tvmaze.com/episodes/1945147/noblesse-1x12-that-all-may-be-as-it-should-be-executionThat All May Be as It Should Be / Execution112.0regular2020-12-2300:002020-12-24T05:00:00+00:0025.0<p>As Raizel heads towards the sanctuary, his path is blocked by a powerful enemy. Meanwhile, Seira, who had been imprisoned, tries to escape to prevent Gejutel's being forced into eternal sleep. In order to protect Seira and Gejutel, Raizel and the others have to fight their own fierce battles. Can they save Seira and Gejutel?</p>NaNhttps://static.tvmaze.com/uploads/images/medium_landscape/292/732080.jpghttps://static.tvmaze.com/uploads/images/original_untouched/292/732080.jpghttps://api.tvmaze.com/episodes/194514749732https://www.tvmaze.com/shows/49732/noblesseNoblesseAnimationJapanese[Anime, Supernatural]Ended25.025.02015-12-042020-12-30https://noblesse-anime.com/00:00[Wednesday]NaN44NaN20.0CrunchyrollNaNNoneNaNNaN386818.0Nonehttps://static.tvmaze.com/uploads/images/medium_portrait/268/670751.jpghttps://static.tvmaze.com/uploads/images/original_untouched/268/670751.jpg<p>Raizel awakens from his 820-year slumber. He holds the special title of Noblesse, a pure-blooded Noble and protector of all other Nobles. In an attempt to protect Raizel, his servant Frankenstein enrolls him at Ye Ran High School, where Raizel learns the simple and quotidian routines of the human world through his classmates. However, the Union, a secret society plotting to take over the world, dispatches modified humans and gradually encroaches on Raizel's life, causing him to wield his mighty power to protect those around him... After 820 years of intrigue, the secrets behind his slumber are finally revealed, and Raizel's absolute protection as the Noblesse begins!</p>1648716882https://api.tvmaze.com/shows/49732https://api.tvmaze.com/episodes/1985214NaNNaNNaNNaNNaN132.0Tokyo MXJapanJPAsia/TokyoNaNNaNNaNNaNNaNNaN